SURF Program Research Opportunities in Gaithersburg, Maryland

host laboratories and Offices

• Communications Technology Laboratory (CTL)
  • some 2025 projects are posted
• Engineering Laboratory (EL)
  • a few 2024 projects shown as examples; one 2025 project posted
• Information Technology Laboratory (ITL)
  • a few 2024 projects shown as examples; one 2025 project posted
• Materials Measurement Laboratory (MML) and the NIST Center for Neutron Research (NCNR)
  • some 2025 projects are posted
• Physical Measurement Laboratory (PML)
  • some 2025 projects are posted
• Special Project Host Offices
  • some 2025 projects are posted
  • Public Affairs Office (PAO)
  • Office of the Associate Director for Management Resources (ADMR)

PAST PROJECTS

A few examples of past projects are shown on this page. Many more are available in the abstract books.
2024 SURF Abstract Book - in person & virtual projects
2023 SURF Abstract Book - in person & virtual projects
2022 SURF Abstract Book - virtual projects only

Communications Technology Laboratory (CTL)

The CTL serves as an independent, unbiased arbiter of trusted measurements and standards to government and industry and focuses on developing precision instrumentation and creating test protocols, models, and simulation tools to enable a range of emerging wireless technologies. The CTL is also home to the National Advanced Spectrum and Communications Test Network (NASCTN), which provides a neutral forum for addressing spectrum-sharing challenges. Learn more about CTL.

Contacts
Lotfi Benmohamed, (301) 975-3650, lotfi.benmohamed [at] nist.gov (lotfi[dot]benmohamed[at]nist[dot]gov)
Wesley Garey, (301) 975-5190, wesley.garey [at] nist.gov (wesley[dot]garey[at]nist[dot]gov)
David W. Griffith, (301) 975-3512, david.griffith [at] nist.gov (david[dot]griffith[at]nist[dot]gov)
Jian Wang, (301) 975-8012, jian.wang [at] nist.gov (jian[dot]wang[at]nist[dot]gov)

CTL Research Opportunities

Wireless Networks Division (Div 673) - 2025 projects

673-1 Performance Analysis of Future Public Safety Communications Networks
Chunmei Liu, 301-975-0454, chunmei.liu [at] nist.gov (chunmei[dot]liu[at]nist[dot]gov)
The United States is in the midst of a transformation of public safety communications networks, fueled by the need to share many modes of digital data (location, sensor data, video, maps, audio, etc.) with and among first responders.  Phones and AR/VR equipment work well in consumer environments, but public safety incidents sometimes involve the loss of communications with base stations (such as in the basement of buildings, or remote wilderness fires) and the infrastructure itself may be damaged or destroyed (e.g., the 2023 Maui fire).  The NIST Wireless Networks Division (WND) is active in the development of next-generation cellular communications standards and research studies around the potential for 5G /6G phones and devices to communicate directly with one another and to serve as relays for disconnected users back to the core network.  Team members are collaborating on the next step of development, the use of multi-hop network relays to complement single-hop direct communications and relays. 
Because the emerging standards are not yet available in implementations, the WND team is building a network simulation and visualization environment to evaluate key metrics of performance such as communications range, latency, throughput, and voice call performance.  The student will learn to use an open source network simulator tool (ns-3) that has been extended by NIST to model public safety networks, and will work within a team of NIST engineers to create simulation scenarios, run simulation campaigns, and analyze and present the results.  The student will gain experience in discrete-event network simulation, programming (C++, Git, Python), and the details of cellular networks.
Desired skills and experience:
Major in Electrical Engineering, Computer Science, or a related field with some programming experience in C++ strongly recommended, and statistical data analysis also recommended.  Background in wireless networking (principles of the Internet and cellular communications), either via a course or previous work experience, is essential.  Experience in using Git for collaborative software development would also be helpful.  The student should be interested in working with computer models and simulations of future networks.  The student should be eager to interact within a team and verbally communicate well. [In-person opportunity]

673-2 Deploying Responsive Monitoring Tools to Cloud Native Deployments
Scott Rose, 301-975-8439, scott.rose [at] nist.gov (scott[dot]rose[at]nist[dot]gov); Oliver Borchert, 301-975-4856, oliver.borchert [at] nist.gov (oliver[dot]borchert[at]nist[dot]gov); Doug Montgomery, 301-975-3630, dougm [at] nist.gov (dougm[at]nist[dot]gov)
5G Open-Radio Access Networks (O-RAN) technologies seek to transform radio access networks from single vendor solutions based upon proprietary appliances to a disaggregated network architecture of components and functions, with standardized open interfaces designed to be deployed in virtualized and cloud native environments. NIST has recently actively engaged in O-RAN Alliance standards development with focus is enhancing the security of virtualized, cloud native, O-RAN functions. We see this area as having both the greatest potential to increase overall network security and the greatest potential risk to the eventual commercial viability of O-RAN technologies. This project will involve deploying monitoring tools to a 5G O-RAN laboratory testbeds to support security standards, and technologies for cloud native virtualization environments (e.g., Kubernetes,  etc.). This includes evaluating ability of existing open-source monitor and logging systems to support security requirements and employing existing open-source tools to actively monitor a workload running in a virtualized environment. In effect, acting as a DevOps security (DevSecOps) team for a O-RAN deployment. This evaluation will include the ability to respond to anomalies that could be indicators of compromise by changing the quantity and quality of monitoring of a specific service or function. Scenarios involving potential malicious activity will be created and used to determine how monitoring systems can detect and help mitigate a potential attack against a telecommunication service infrastructure. The goal is to produce guidance and tools on tailoring monitoring services to a cloud based deployment.
Desired skills / experience: 
Linux, Kubernetes / Docker, service-based architectures, some programming (Python, golang, NodeJS, or similar), dev-ops / network programming, network protocols / tools / technologies (http, TLS, PKI, OAUTH, Wireshark), security scanning tools. [In-person opportunity]

673-3 Performance Analysis and Optimization of ISAC Systems
Jack Chuang and Jian Wang, 301-975-4171 and 301-975-8012, jack.chuang [at] nist.gov (jack[dot]chuang[at]nist[dot]gov) and jian.wang [at] nist.gov (jian[dot]wang[at]nist[dot]gov)
Integrated sensing and communications (ISAC) will be key for next-generation wireless communication systems. ISAC uses a sensing-enabled infrastructure to support a wide range of new wireless applications such as UAV intrusion detection, UAV flight trajectory tracing, traffic monitoring, automobile navigation assistance, at-home health monitoring, and many more. To perform sensing, the receiver examines the communication signals from the transmitter that are reflected off target objects. The receiver uses the reflected signals to estimate target parameters such as range, angle, and velocity. ISAC system design can be challenging due to limited radio resources, dynamic channel conditions, trade-offs between the radio resources allocated to the sensing and communication functions, and real-time variations in the demand for the sensing and communication resources. The Wireless Networks Division in the Communications Technology Laboratory at NIST is helping to develop standards for ISAC in next-generation wireless networks by conducting research that includes target modeling, sensing algorithms design, sensing reporting, and sensing performance evaluation. In this project, the student will collaborate with NIST researchers to evaluate ISAC sensing capabilities. They will identify the key factors that impact ISAC performance and design signals and transmission schemes to maximize sensing performance while satisfying the communications system’s requirements. 
Desired Skills: Major in Electrical Engineering, Computer Science, or a related field; proficiency in MATLAB and/or Python programming languages; experience in digital signal processing; familiarity with Git version control; and strong communication skills. [In-person Opportunity]

Smart Connected Systems Division (Div 674) - 2025 projects

PROJECT WITHDRAWN: 674-1 Generating Datasets Using a Reverberation Chamber for Industrial Wireless

674-2 IEEE 1451-based IoT Smart Sensor Networks for Real-Time Environment Monitoring
Eugene Song, 301-975-6542, eugene.song [at] nist.gov (eugene[dot]song[at]nist[dot]gov)
Smart sensors and sensor networks are used everywhere in Internet of Things (IoT) applications to enable real-time monitoring and control for improved reliability and resilience. However, the interoperability of smart sensor data is a major challenge for various IoT applications. Adoption of standards for IoT sensor networks can improve sensor data interoperability, such as the IEEE 1451.0-2024 standard which defines common functions of IoT sensor network components, network services, sensor services, and Transducer Electronic Datasheet (TEDS) formats. IEEE P1451.1.6 defines a method for transporting IEEE 1451.0 services messages over a user network using Message Queue Telemetry Transport (MQTT) to achieve sensor interoperability for IoT applications.  
This project will focus on building IEEE 1451-based IoT smart sensor networks for real-time environment monitoring of smart buildings.  The student will study the IEEE 1451.0-2024 and P1451.1.6 MQTT standards for smart sensors, design and setup smart sensor networks using the Raspberry Pi development kit and Node-RED IoT platform, develop smart sensor nodes and application nodes based on 1451.0 and P1451.1.6 standards using Python, test and display real-time environment monitoring results of a NIST laboratory, and draft a technical report on the project results. This work will be performed collaboratively with a team of NIST researchers at the NIST Gaithersburg site.
Skills: Python programming experience required. Computer Engineering (CE) or Electric and Electronic Engineering (EE) major preferred. Experience using GitHub, Raspberry Pi development kit, and Node-RED platform preferred. [In-person opportunity]

674-3 Automated Vehicle Comfort Evaluation
Wendy Guo, 301-975-5855, %20wenqi.guo [at] nist.gov (wenqi[dot]guo[at]nist[dot]gov)
As automated vehicle (AV) technology continues to evolve, the focus has expanded beyond addressing technical challenges like navigation, safety, and reliability to improving the overall passenger experience. Passenger comfort has become a key consideration in the widespread adoption of AVs, especially as they become more common in personal transportation, ride-sharing, and public transit. Passenger comfort in AVs is influenced by several factors, including vehicle dynamics (acceleration, braking, and turning), road conditions, cabin ergonomics, and the vehicle’s interaction with its network environment. Accurately evaluating these factors requires advanced tools and methodologies. Industry standards, such as ISO 2631, provide critical guidelines for acceptable levels of vibration and acceleration, ensuring human comfort. 
In this project, you will have the opportunity to engage with the existing AV simulation testbed established within our group. You will learn to utilize this testbed to generate data across a range of testing scenarios. Additionally, you will have the chance to apply machine learning algorithms to analyze various datasets, identifying patterns in driving behavior and their effects on passenger comfort. Ultimately, you will be able to compare the data you collect from the simulation testbed with real-world testing results by using a physical autonomous vehicle.
Desired skills: Experience with object-oriented programming is required. Computer science or network engineering major is preferred. Interested in automated vehicle testing and evaluation. [In-person preferred]

674-4 SysML Extension for Physical Interaction and Signal Flow (SysPhS) Library Development
Charlie Manion, 301-975-4251, charles.manion [at] nist.gov (charles[dot]manion[at]nist[dot]gov)
This project provides an opportunity for a student to learn systems modelling in the newly developed Systems Modeling Language v2 (SysML2) standard by actively contributing to development of the SysPhS standard, an extension of SysML for 1D modeling and simulation. SysML is a widely-used standard for describing complex systems, such as spacecraft, naval vessels, and manufacturing systems, enabling large engineering teams to collaborate in designing them.
SysPhS adds 1D modeling to SysML and defines translation to 1D simulation tools, such as OpenModelica and Mathworks Simulink/Simscape.  This kind of modeling assembles physical and control components that include ordinary differential and differential algebraic equations, typically taking derivatives of functions only of time, forming a system of equations solved by time-stepped simulators.  It is applicable to a wide variety of cyberphysical systems, including electrical, mechanical,  hydraulic, and thermal. This project will focus on developing more physical and control component libraries to increase the capabilities of SysPhS.
The student will: (a) Learn how to model in SysML 2 and SysPhS. (b) Develop new physical/control component libraries for SysPhS in SysML 2. (c) Test compatibility of the new libraries on multiple modeling and simulation platforms including Modelica and Simulink/Simscape. (d) Develop examples demonstrating modeling and analysis with these libraries.
Skills- Preferred major: Mechanical, Aerospace, or Electrical Engineering. Required: Calculus based physics or dynamics or circuit theory. Basic programming experience such as with Python or Matlab. Recommended: Experience with 1D modeling and simulation such as Modelica or Simulink. Has taken Numerical methods, linear systems and signals, or controls. Some familiarity with object oriented programming. [In-person or virtual opportunity]

674-5 Industrial Artificial Intelligence Management and Metrology: Human AI Teaming Testing and Analysis
Michael Sharp, 301-975-0476, michael.sharp [at] nist.gov (michael[dot]sharp[at]nist[dot]gov)
This project offers an opportunity for a student to work on testing and analyzing technical language processing methods to enhance human and AI collaboration in industrial environments. The student will focus on developing tools that improve human-AI teaming, particularly in contexts where technical documents, instructions, and communications are key. Under the guidance of experts in AI and software development, the student will assist in creating and evaluating systems that allow AI to better understand, process, and interact with complex technical language used in industrial communications and processes.
The student will work with real-world and simulated data to: (a) Test and refine AI-driven systems designed to interpret technical documents, standard operating procedures, and instructions. (b) Explore methods for optimizing AI's ability to communicate effectively with human workers, especially in technical and industrial contexts. (c) Implement and test models that analyze and process language to streamline collaboration between human teams and AI systems. (d) Assist in the creation of metrics to evaluate the effectiveness of AI’s role in improving comprehension, task execution, and decision-making in industrial environments.
This project aims to enhance productivity by developing intuitive and effective ways for AI systems to support human workers in complex technical fields.
Skills- Required: Good communication skills; Python coding and/or significant coding experience; beyond high school level classes in engineering, computer science, or statistics; ability to work with a team. Recommended: Experience working collaboratively with Git, specifically GitLab; technical writing experience; knowledge of technical risk & reliability for production processes; understanding of basic AI methodologies and AI software packages. [In-person or virtual opportunity]

674-6 Industrial Artificial Management and Metrology: System Simulation and Evaluation
Michael Sharp, 301-975-0476, %20michael.sharp [at] nist.gov (michael[dot]sharp[at]nist[dot]gov)
This project provides a student the opportunity to assist in the development of SimPROCESD, a discrete event simulator designed for modeling and testing multistage manufacturing processes. The student will focus on coding and refining simulation tools that allow for the analysis of complex production workflows, aiming to optimize efficiency and identify bottlenecks in industrial systems. 
Working closely with software development experts, the student will contribute to the creation of a simulation framework tailored for manufacturing environments, allowing for the testing of different production scenarios and the evaluation of process performance.
Key tasks will include: (a) Writing and optimizing code for SimPROCESD to simulate various manufacturing processes. (b) Collaborating with engineers and developers to ensure the simulator accurately models real-world production systems. (c) Implementing features for testing 'what-if' scenarios to assess the impact of changes in production parameters. (d) Developing tools to visualize and analyze simulation data to identify areas for process improvement. (e) Assisting in the integration of real-time data from physical systems into the simulator for more accurate testing and monitoring.
This project aims to provide practical insights into the design and testing of advanced manufacturing systems, helping industry professionals streamline operations through effective simulation and process analysis.
Skills: Required: Good communication skills; Python coding and/or significant coding experience; beyond high school level classes in engineering, computer science, or statistics; ability to work with a team. Recommended: Experience working collaboratively with Git, or GitLab; technical writing experience; knowledge of technical risk & reliability for production processes. [Virtual opportunity]

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Engineering Laboratory (EL)

The EL promotes the development and dissemination of advanced manufacturing and construction technologies, guidelines, and services to the U.S. manufacturing and construction industries through various activities in areas such as fire prevention and control; national earthquake hazards reduction; national windstorm impact reduction; national construction safety teams; and building materials and structures. Learn more about EL.

Contact
Cartier P. Murrill, (301) 975-5738, cartier.murrill [at] nist.gov (cartier[dot]murrill[at]nist[dot]gov)

EL Research Opportunities

Engineering Laboratory Office (Div 730) - 2024 project shown as example

Community Resilience Planning Sentiments Following a Natural Hazard Event
Christina Gore, christina.gore [at] nist.gov (christina[dot]gore[at]nist[dot]gov), and Jennifer Helgeson, jennifer.helgeson [at] nist.gov (jennifer[dot]helgeson[at]nist[dot]gov)
This project will evaluate changes in discussions surrounding increasing resilience to future natural hazard events immediately following a community experiencing a natural hazard event. The analysis will include data from Google Trends as well as X (formerly known as Twitter) data. Google trends data will be used to show the key words that are commonly searched for following a natural hazard event. Those keywords will also help inform the data set of tweets used for analysis. The tweets will then be coded based on the types of sentiments that are expressed by the tweets and that data will be analyzed. 
Required Qualifications/Skills: Experience with statistics is highly encouraged. Qualitative text analysis experience is valued. Strong analytical skills and writing skills are preferred. Familiarity with community resilience through economics, psychology, sociology, engineering, or other related disciplinary or interdisciplinary courses. Interest in community resilience and sustainability. [In-person opportunity]

Materials and Structural Systems Division (Div 731) - 2024 project shown as example

Sulfur Analysis Pyrrhotite in Aggregate and Concrete
Stephanie Watson, stephanie.watson [at] nist.gov (stephanie[dot]watson[at]nist[dot]gov), and Lipiin Sung, li-piin.sung [at] nist.gov (li-piin[dot]sung[at]nist[dot]gov)
Damage to concrete building structures in Connecticut was attributed to iron sulfide mineral pyrrhotite and results in decomposition and structure cracking. States (CT, MA) are passing building and DOT codes to prevent this issue, but there are no standardized methods or concentration limits to assess pyrrhotite abundance. NIST developed reference standards (RM) to provide an accurate, consistent pyrrhotite analysis in concrete. This project focuses on optimizing sulfur analysis using an induction furnace combustion method to quantify total sulfur species in RMs and foundation specimens.  This study will optimize the use of chemical reagents to ensure complete burn for cementitious systems.
Required Qualifications/Skills: Background knowledge and training in engineering or physics, or chemistry. Courses in chemistry (general and organic) and/or physics, and mathematics courses (algebra and calculus) are required. Computer skills, Microsoft Office programs such as Word, Excel and Powerpoint, are also required. Skill for "data analysis, interpretation of measurements results; plotting data" is a plus. [In-person opportunity]

Building Energy and Environment Division (Div 732) - 2025 project

732-1 Tandem Hyperspectral Photoluminescence-electroluminescence Imaging Technique for Defect Characterization in Wide Band Gap Power Devices
Behrang Hamadani, 301-975-5548, behrang.hamadani [at] nist.gov (behrang[dot]hamadani[at]nist[dot]gov)
(CHIPS) Wide band gap (WBG) semiconductors are key material components in the next generation of power electronics. Great strides have been made in their synthesis and integration into power devices structures; however, characterization methods for probing performance-degrading defects are underdeveloped. To extract maximal information on the spatial and electronic nature of these defects, we employ a novel tandem imaging technique that uses hyperspectral photoluminescence in conjunction with electroluminescence. In doing so, we achieve unprecedented insight into spectral deviations that correspond to directly observable emission anomalies. Data collection and interpretation is complex and multifaceted, with hundreds of thousands-to-millions of spectra produced in each image. The goal of this project is to use the tandem-imaging technique on several WBG materials and devices and use simple modeling to interpret the data. [In-person opportunity] 

Fire Research Division (Div 733) - 2024 project shown as example

Material Flammability Apparatus Development and Testing
Isaac Leventon, isaac.leventon [at] nist.gov (isaac[dot]leventon[at]nist[dot]gov), and Michael Heck, michael.heck [at] nist.gov (michael[dot]heck[at]nist[dot]gov)
The Engineered Fire Safe Products (EFSP) Project in the Fire Research Division at NST is focused on the development and application of the capabilities (experimental & computational analysis tools) to enable quantitative prediction of material flammability behavior (e.g., ignition, steady burning, fire growth).
This SURF project will focus on the construction and calibration of a miniaturized gasification apparatus (one of the bench scale apparatus needed to maintain these capabilities).
Required Qualifications/Skills: Mechanical Engineering, Fire Protection Engineering, Physics, Hands-on lab experience (calibrating equipment, running experiments, electrical/circuit work) [In-person opportunity] 

Systems Integration Division (Div 734) - 2024 project shown as example

Forecasting Rare Earth Element (REE) Demand for Use in Clean Energy Technologies
Nehika Mathur, nehika.mathur [at] nist.gov (nehika[dot]mathur[at]nist[dot]gov), and Matthew Triebe, matthew.triebe [at] nist.gov (matthew[dot]triebe[at]nist[dot]gov)
Clean energy technologies (e.g., solar, wind, EVs) are vital in our transition to a decarbonized energy grid. Many clean energy technologies rely on Rare Earth Elements (REEs) several of which are prone to supply chain risks. As the demand for clean energy technologies grows, so will the demand for REEs. Anticipating REE market dynamics becomes crucial for change makers in developing effective strategies to scale up the implementation of clean energy generating technologies. This project aims to identify REEs critical to a clean economy and subsequently aims to determine demand quantities (till 2040) for these materials via a forecasting model.
Required Qualifications/Skills: The ideal candidate will be pursuing a degree in mechanical, industrial or chemical engineering with experience coding in R and/or Python. An understanding of the manufacturing sector is desirable. An interest in the circular economy will be beneficial. [Virtual opportunity]

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Information Technology Laboratory (ITL)

The ITL focuses on information technology (IT) measurements, testing, and standards and is a globally recognized and trusted source of high-quality, independent, and unbiased research and data. As a world-class measurement and testing laboratory encompassing a wide range of areas of computer science, mathematics, statistics, and systems engineering, ITL’s strategy is to maximize the benefits of IT to society through a balanced IT measurement science and standards portfolio of three main activities: fundamental research in mathematics, statistics, and IT; applied IT research and development; and standards development and technology transfer. Learn more about ITL.

Contacts
Yolanda Bursie, (301) 975-6738, yolanda.bursie [at] nist.gov (yolanda[dot]bursie[at]nist[dot]gov)
Derek Juba,(301) 975-5518 derek.juba [at] nist.gov (derek[dot]juba[at]nist[dot]gov)

Software and Systems Division (Div 775) - 2025 project

775-1 Artificial Intelligence for Atomic Scale Scanning Electron Microscopy-based Dimensional Measurements of Integrated Circuit Structures
Peter Bajcsy and Andras Vladar, 301-975-2985, peter.bajcsy [at] nist.gov (peter[dot]bajcsy[at]nist[dot]gov) and andras.vladar [at] nist.gov (andras[dot]vladar[at]nist[dot]gov)
(CHIPS) The first phase of this project will explore multiple image quality metrics to compare simulated and measured images, methods for estimating the uncertainty of image-derived measurements of linear features, and correlations between image quality metrics and measurement uncertainty estimates.  The image quality metrics can be computed in real time and fed back to the SEM instrument to optimize imaging parameters while delivering low uncertainty (high-quality) image-based measurements. The project's second phase will design a Denoising Diffusion Probabilistic Model (DDPM) that will improve the image quality by supervised learning of the noise distribution and, hence, lower the measurement uncertainty. A diffusion process in DDPM iteratively uses linear interpolation to create noisy images from clean images, similar to real measured images. The model is trained to generate noisy images by learning the real noise characteristics. By reversing the process, the model can be trained to denoise images. By designing a well-performing model for image denoising, one can lower the number of repeated imaging acquisitions while still delivering high-quality image-based measurements with low uncertainty. [In-person opportunity] 

Statistical Engineering Division (Div 776) - 2025 project

776-1 Calibration and Validation of X-Ray CT Simulation for Advanced Packaging
Adam Pintar, 301-975-4554, adam.pintar [at] nist.gov (adam[dot]pintar[at]nist[dot]gov) 
(CHIPS) This project has two connected parts. The primary part will be to tune the parameters of the aRTist X-Ray computed tomography (XCT) simulation software to match as closely as possible data from a real XCT scan of semiconductor advanced packaging features, e.g., Through Silicon Vias (TSVs). The second part will use the calibrated simulation software to assess the effect of measurement error on a hat versus a probability of defect detection studies.
XCT scans of a chip with TSVs revealed typical defects resulting from the electrodeposition process. These scans and active learning will be used to calibrate the adjustable parameters of the aRTist XCT simulation software. To apply Active Learning, it will be necessary to reduce the scans and simulation results to one or only a few summary numbers. How best to accomplish the reduction will be a part of the research, and multiple summaries will be compared. The student’s responsibilities will include modifying existing Python scripts to run the aRTist software and using Python or R packages for active learning.
Given sufficient time, the project will have a second part. The calibrated simulation software will be used to simulate XCT reconstructions of TSVs with defects of known sizes. The simulations will provide a corresponding measured defect size for each true defect. A common technique for estimating the probability of detection for a defect of a given size is known as an a hat versus a study, where a true defect size is compared to its measurement. Simulations are a perfect tool for assessing the accuracy of a hat versus a studies because in simulations, we can know the true defect size, but in real situations there will always be uncertainty. The effect of that uncertainty on a hat versus a studies will be the focus of our assessments. The student’s responsibilities will include image processing with Python and fitting lines and measurement error models with either Python or R. [In-person opportunity] 

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Material Measurement Laboratory (MML) and the NIST Center for Neutron Research (NCNR)

The MML serves as the national reference laboratory for measurement research, standards, and data in the chemical, biological and material sciences, and conducts research in analytical chemistry, biochemical science, ceramics, chemical and biochemical reference data, materials reliability, metallurgy, polymers, surface and microanalysis science, and thermophysical properties of materials. MML research supports areas of national importance including but not limited to advanced materials, electronics, energy, the environment, food safety and nutrition, and health care. Learn more about MML.

The NCNR is a major national user facility and resource for industry, universities, and government agencies with merit-based access made available to the entire U.S. technological community. Neutron-based research covers a broad spectrum of disciplines, including engineering, biology, materials science, chemistry, physics, and computer science. Current experimental and theoretical research is focused on materials such as polymers, metals, ceramics, magnetic materials, porous media, fluids and gels, and biological molecules. Learn more about the NCNR.

The MML/NCNR program is specifically designed to provide hands-on research experience in three topic areas: Chemical/Biochemical Sciences, Computational Materials Science, and Materials Science. All SURF projects at the NCNR will be conducted in person.

Contacts

MML
Jackie Mann, %20jmann [at] nist.gov (jmann[at]nist[dot]gov)
Mark McLean, mark.mclean [at] nist.gov (mark[dot]mclean[at]nist[dot]gov)
Dan Siderius, daniel.siderius [at] nist.gov (daniel[dot]siderius[at]nist[dot]gov)

NCNR
Julie A. Borchers, (301) 975-6597, julie.borchers [at] nist.gov (julie[dot]borchers[at]nist[dot]gov)
Leland Harriger, (301) 975-8360, leland.harriger [at] nist.gov (leland[dot]harriger[at]nist[dot]gov)
Yimin Mao, (301)975-6017, yimin.mao [at] nist.gov (yimin[dot]mao[at]nist[dot]gov)
Susana Teixeira, (301)975-4404, scm5 [at] nist.gov (scm5[at]nist[dot]gov)

MML & NCNR Research Opportunities

CHEMICAL/BIOCHEMICAL SCIENCES: This concentration addresses the nation's needs for measurements, standards, technology development, and reference data in the areas broadly encompassed by chemistry, biotechnology, and chemical engineering.

NIST Center for Neutron Research - Neutron Condensed Matter Science Group (Div 610.02)  - 2025 projects

610.02-03 Supported Membranes to Measure Antibiotic Interactions
David Hoogerheide, dph [at] nist.gov (dph[at]nist[dot]gov)
While automation and high-throughput experimentation have fueled unprecedented innovation, antibiotic resistance remains a global threat. The first thing a drug sees when it reaches a cell is the membrane, making it a prime target for antibiotics. In this project, the student will use automated liquid handling to make model cell membranes on sensor surfaces for characterization using biophysical techniques such as quartz crystal microbalance. The student will test different surface chemistries, learn about the physics of the interactions between that surface and a membrane, and explore the effects of antimicrobial peptide preparations on membranes. The project lies at the intersection of chemistry, biology, and physics; this work will help expand our toolkit for studying membranes and combatting antibiotic resistance. [In-person opportunity]

610.02-04 Nanostructure, Viscosity, and High-shear Processing of Biodegradable Polymer Solutions
Ryan Murphy, rpm1 [at] nist.gov (rpm1[at]nist[dot]gov)
Biodegradable plastics containing modified cellulose polymers, such as hydroxypropyl methylcellulose (HPMC), are of significant industrial interest due to their biocompatibility and natural degradation pathways in water at their end-of-application. HPMC polymer additives are employed industrially to modify the viscosity and flow-characteristics of many water-based products. HMPC is also known to form thermoreversible, self-assembled fibers in water, which can percolate to form kinetically-trapped gel nanostructures. Despite this widespread industrial use, the high-shear viscosity and corresponding nanostructure remains unexplored. This project aims to utilize a unique capillary rheometer to measure the high-shear viscosity as a function of important processing variables including molecular weight, concentration, and temperature. The nanostructure of HPMC solutions and gels will be measured under flow using x-ray or neutron scattering methods to develop important structure-property relationships. These measurements will help guide polymer processing operations by establishing pathways to tune their flow-response and degradation kinetics. [In-person opportunity]

610.02-05 Advancing Lipid Nanoparticle Standardization: Structural Characterization and Analytical Techniques for mRNA Delivery Systems
Chelsea Edwards, cee [at] nist.gov (cee[at]nist[dot]gov)
Lipid nanoparticles (LNPs) are a critical new drug delivery platform attracting substantial R&D investment across the pharmaceutical industry to develop novel vaccines, cancer therapies, and genetic disease treatments. Scientists at NIST are developing a research-grade test material (RGTM), consisting of an mRNA-containing LNP solution, to support the standardization of analytical techniques for characterizing LNP drugs across the industry. Many questions remain about the properties and structure of LNPs and how they can be influenced by components, compositions, and mixing procedures. An improved understanding of various structural aspects of LNPs will help enable the rational design of future LNP drugs. This project is focused on characterization of LNPs using various analytical techniques to complement neutron scattering measurements that characterize their structure and support development of the RGTM. Depending on the student’s interests/abilities, the project can include LNP synthesis and preparation, at least one light scattering technique, biological assays, and data analysis. The project is based at the Center for Neutron Research in collaboration with scientists at other divisions of NIST. If the student is prolific, they will also gain practice making figures and writing up their portion of the results for publication in a peer-reviewed journal. [In-person opportunity]

610.02-08 Understanding the Solution Properties of Model Liposomal Drug Solutions
Elizabeth Kelley and Ryan Murphy, elizabeth.kelley [at] nist.gov (elizabeth[dot]kelley[at]nist[dot]gov )and ryan.murphy [at] nist.gov (ryan[dot]murphy[at]nist[dot]gov)ryan.murphy [at] nist.gov ( )
Liposomes are nanosized, hollow particles enclosed by a lipid bilayer. They are used as model systems for biomembranes, small volume chemical reactors, and containers for hydrophilic guest molecules in personal care products, drug delivery, and vaccines. In almost all these applications, the liposomes are made a specific lipid concentration. Yet the total volume fraction of liposomal particles is significantly higher than that of the lipid molecules because the liposomes are filled with solvent. These high liposomal particle concentrations can significantly affect the properties of the solutions that influence manufacturing and delivery processes. As such, this project will systemically explore how the macroscopic solution properties depend on the fundamental properties of liposomes in model drug solutions. [In-person opportunity]

Materials Science and Engineering Division (Div 642) - 2025 project

642-06 Developing Low Dielectric Epoxy Test Materials for Electronics Packaging
Andrew Korovich, 917-549-5971, andrew.korovich [at] nist.gov (andrew[dot]korovich[at]nist[dot]gov)
(CHIPS) In this SURF project, the student will assist in developing and testing formulations for a low Dk/Df epoxy resin system alongside our efforts to develop a research grade test material that will be used as a benchmark for our metrologies related to understanding structure property relationships of polymeric materials used in advanced semiconductor packaging. We’re looking for a student with a background in chemistry, engineering or material science who is interested in gaining hands on experience in planning and conducting experiments in a lab setting. The student will be provided with the necessary training, equipment, and environment to conduct their research safely and in adherence to NIST safety rules. [In-person opportunity]

Materials Measurement Science Division (Div 643) - 2025 project

643-05 Personalized Medicine and Point-of-Care Pharmaceutical Manufacturing
Tom Forbes, 301-975-2111, thomas.forbes [at] nist.gov (thomas[dot]forbes[at]nist[dot]gov)
This project is investigating various measurement science and standards for avenues related to distributed and point-of-care pharmaceutical production, focused on additive manufacturing schemes. Project opportunities for the summer may focus on metrology for polypill formulation/production and quality assurance, personalized medicine for tapering regimens, orodispersible film formulation/fabrication, and/or process analytical technologies for atline/inline measurements. Analytical technologies including microdrop UV-Vis, fluorescence, and Raman spectroscopies, liquid chromatography, electrophoresis, mass spectrometry, and more may be used for analysis. Depending on student interest and background, opportunities related to developing machine learning process monitoring, anomaly detection, and other computationally driven data analysis schemes also exist. General laboratory, personal protective equipment, chemical handling, and laser safety training will be conducted, as well as experimental procedure specific hazard reviews. [In-person opportunity]

Biosystems and Biomaterials Division (Div 644) - 2025 project

644-03 MML Webpage AI Readiness
Talapady Bhat, 301-975-5448, talapady.bhat [at] nist.gov (talapady[dot]bhat[at]nist[dot]gov)
The student will assess Genini AI's responses to topics relevant to MML webpages, evaluating their quality and accuracy. Based on this evaluation, they will identify potential shortcomings or areas for improvement in the AI-generated content and formulate hypotheses to address these issues. The student will then implement changes to the webpages, such as refining prompts, adjusting AI parameters, or modifying the content itself. After making these modifications, they will re-evaluate the AI-generated content to assess the impact of the changes. This iterative process will help to continuously improve the AI readiness of MML webpages. [Virtual opportunity]

Biomolecular Measurement Division (Div 645) - 2025 project

645-01 Mammalian Cell Counting Techniques
Ioannis Karageorgos, 240-314-6337, ioannis.karageorgos [at] nist.gov (ioannis[dot]karageorgos[at]nist[dot]gov)
The most common method for cell counting is a classic hemocytometer. Advancements in imaging technologies have enabled the automation of cell counting, providing improved accuracy and reliability. In this project we will use a variety of cell counters like Orflo, Vicell , TC10 and hemocytometer  to perform measurements on NISTCHO cell line and test these technologies. 
The student will get trained  to work under a biosafety cabinet (BSC) using aseptic cell culture techniques. The student will learn to operate a variety of cutting edge cell imaging systems. [In-person opportunity]

Chemical Sciences Division (Div 646) - 2024 project shown as example

Developing Tools to Help BBD/MML Web Pages AI Ready
T N Bhat, 301-975-5448, talapady.bhat [at] nist.gov (talapady[dot]bhat[at]nist[dot]gov)
Chat-GPT, LLM and AI has become a common household topic of interest to everyone. During the year 2023 my SURF and SHIP students worked to evaluate the performance of Chat-GPT to approximately 500 Covid-19 related question and answers. This study revealed that Chat-GPT answers are just about 30% accurate to the question. A primary reason for this poor performance of Chat-HPT is poor quality of the reference documents available for LLM to generate accurate answers. In 2024 students will develop tools to further evaluate Chat-GPT and LLM and suggest mitigative measures to alter reference documents. [Virtual opportunity]

COMPUTATIONAL MATERIALS SCIENCE: This concentration includes the application of modeling, simulation, and computational methods to enhance our understanding of innovative materials and devices. This concentration includes projects within the Materials Genome Initiative.

NIST Center for Neutron Research - Reactor Operations and Engineering Group (Div 610.01) - 2024 project shown as example

Development of an Intelligent Monitoring System for the Cold Neutron Source Cryogenics System at the NBSR
Robert Newby and David Hix, (301)975-8645, rnn1 [at] nist.gov (rnn1[at]nist[dot]gov)
The student will work towards the development of an intelligent and user-friendly monitoring system for the cold neutron source (CNS). The student will mainly be responsible for understanding the existing CNS system and its sensors and alarms, and then leveraging that knowledge to develop an intelligent condition monitoring system that can discern alarm causes and report them to reactor engineering and operations staff in an accessible manner. Depending on the student’s capabilities, a machine learning algorithm may also be pursued for analyzing historical data logs to develop automated early fault prediction capabilities for the CNS. The student will be gaining experience in developing front-end and back-end of applications for engineering systems. The student will also be gaining valuable insight into the operation of a state-of-the-art CNS at a nuclear test reactor. [In-person opportunity]

NIST Center for Neutron Research - Neutron Condensed Matter Science Group (Div 610.02) - 2024 project shown as example

Triple-axis Automation
William Ratcliff, (301)975-4316, william.ratcliff [at] nist.gov (william[dot]ratcliff[at]nist[dot]gov)
In this project, you will work on using AI to automate the use of an instrument at our international user facility.  The instrument is a thermal triple axis, which measures superconductors, magnetic materials, and materials for quantum information.  You will use Bayesian optimization and reinforcement learning to automate the alignment of crystals and the taking of data.  Your work will accelerate the pace of science and discovery. [In-person opportunity]

Materials Science and Engineering Division (Div 642) - 2025 projects

642-01 Density Functional Theory Study of the Electronic and Magnetic Properties of Two-dimensional (2D) Materials
Daniel Wines, 301-975-2542, %20daniel.wines [at] nist.gov (daniel[dot]wines[at]nist[dot]gov)
Two-dimensional (2D) materials such as monolayer transition metal dichalcogenides and transition metal dihalides are an emerging class of nanomaterials that can be used for a wide variety of electronic and magnetic applications. This project will focus on systematically studying the 2D structures using density functional theory (DFT). The results of these calculations will be uploaded as a part of the Joint Automated Repository for Various Integrated Simulations (JARVIS, https://jarvis.nist.gov) DFT database hosted here at NIST. The student will specifically focus on 2D magnetic materials such as VSe2 and NiI2, using a variety of approximations in DFT to benchmark how different levels of theory impact the material properties. These properties include the preferred magnetic ground state, electronic band structure and magnetic transition temperatures. The student will also assist in writing high-throughput workflow scripts to carry out these DFT calculations and post-processing of calculated data. [In-person opportunity]

642-02 Leveraging Density Functional Theory and Machine Learning to Study Defects in Wide Band Gap Materials
Daniel Wines, 301-975-2542, %20daniel.wines [at] nist.gov (daniel[dot]wines[at]nist[dot]gov)
(CHIPS) Wide band gap materials such as gallium nitride (GaN) are crucial for next-generation electronics. The presence of defects can dramatically impact device performance by influencing the electronic and transport properties. This project will utilize density functional theory (DFT) to study defect formation energies and defect-induced electronic states in wide band gap materials. The results of these calculations will be uploaded as a part of the Joint Automated Repository for Various Integrated Simulations (JARVIS, https://jarvis.nist.gov) DFT database hosted here at NIST. The student will perform calculations for defective semiconductors using a variety of approximations in DFT to benchmark how different levels of theory impact the material properties. The student will also utilize machine learning models, specifically the Atomistic Line Graph Neural Network (ALIGNN) to accelerate electronic property predictions of defect configurations. Specifically, the student will assist in writing high-throughput workflow scripts to carry out these DFT calculations and post-processing of calculated data in addition to training ALIGNN models on DFT data and benchmarking the accuracy of these models. [In-person opportunity]

642-04 Evaluating Large Language Models for Inverse Materials Design
Kamal Choudhary, 301-975-4393, kamal.choudhary [at] nist.gov (kamal[dot]choudhary[at]nist[dot]gov)
(CHIPS) The selected candidate will analyze the performance of various LLMs, comparing their predictions to experimental or computational datasets, and upload their results on the JARVIS Leaderboard benchmarking platform. Responsibilities include preprocessing datasets, running and analyzing models, and documenting findings for internal presentations and potential publications. Preferably, applicants should be pursuing a degree in materials science, computer science, or a related field and possess experience with Python, machine learning libraries, and materials property concepts. [In-person opportunity]

642-05 Machine Learning Enabled Bandgap Electron Energy Loss Spectra of Thickness Varying Lamellae Lifted out from Power Electronics
W. C. David Yang, 301-975-8398, david.yang [at] nist.gov (david[dot]yang[at]nist[dot]gov)
(CHIPS) Wide bandgap (WBG) materials are emerging semiconductors in future power electronics. Recent advancements in synthesis and device integration aim to engineer structural defects during material growth to suppress performance-limiting defects. However, defect metrology for power electronics is underdeveloped in correlating defect structures to local optoelectronic properties. Measuring these local property parameters is critical to validate models built to elucidate the defect’s impact on overall device performance. We use electron energy loss spectroscopy (EELS) in a transmission electron microscope (TEM) to measure the property parameters, like bandgap, of WBG semiconductors in a lamella lifted out from power devices. The varying lamellae thickness convolutes the EELS resolution function and causes bandgap measurement uncertainty. The project aims to develop a machine learning (ML) model to decouple the influence of thickness variation from bandgap EEL spectra, which can be explicitly used to characterize defect states within bandgaps. [In-person opportunity]

642-08 Optimizing and Expanding Quantum Computing Workflows for Chemistry Simulations
Nia Pollard, nia.rodney-pollard [at] nist.gov (nia[dot]rodney-pollard[at]nist[dot]gov)
This project offers two primary research directions for undergraduate students. First, students will work on migrating an existing Python-based quantum computing code to the latest version of Qiskit, ensuring compatibility with current quantum hardware and simulators. This task includes updating deprecated functions, validating the workflow, and testing its performance on available quantum backends. Second, students will explore the impact of varying key parameters within the workflow, such as active space size, initial states, noise mitigation techniques, or the number of repetitions in ansatz circuits, to analyze trends and optimize performance. Additionally, students can investigate the effects of noise using IBM noise models and test error mitigation strategies to improve accuracy. Opportunities also exist to create data visualization tools for presenting results and to contribute to the development of educational resources or tutorials based on the workflow. These research directions provide a comprehensive introduction to quantum computing applications in chemistry and materials science while equipping students with valuable programming and data analysis skills. [In-person or virtual opportunity]

642-09 Molecular Dynamics Simulation Protocol for Analysis of Reverse Osmosis Polyamide Membranes
Ryan Nieuwendaal, 301-975-6766, ryan.nieuwendall [at] nist.gov (ryan[dot]nieuwendall[at]nist[dot]gov)
The student will assist in the development of molecular dynamics (MD) simulations for vetting candidate structures with experimental solid-state NMR data using High Performance Computing (HPC) systems. The crosslinking protocol will use the Membrfactory [1] script run in Python which utilizes the GROMACS software package [2 – 4]. Pair-wise distribution functions will be used to simulate solid-state NMR data, which will be calculated by atom positions that result from MD simulations performed as a function of crosslink density. Once established (and time pending), water dynamics inside the polyamide structures will be calculated from autocorrelation functions that result from the MD simulations; orientational autocorrelation times will be related to magnetic dipole-dipole and quadrupolar interactions from NMR measurements, and spatial autocorrelation times related to quasi-elastic neutron scattering measurements.  [In-person or virtual opportunity]
References
[1] K. Li, S. Li, W. Huang, Ch. Yu, “MembrFactory: A Force Field and composition Double Independent Universal Tool for Constructing Polyamide Reverse Osmosis Membranes,” J. Comp. Chem. 2019, 40, 2432-2438.
[2] H. J. C. Berendsen, D. van der Spoel, R. van Drunen, Comput. Phys. Commun. 1995, 91, 43.
[3] D. van Der Spoel, E. Lindahl, B. Hess, G. Groenhof, A. E. Mark, H. J. Berendsen, J. Comput. Chem. 2005, 26, 1701.
[4] B. Hess, C. Kutzner, D. van Der Spoel, E. Lindahl, J. Chem. Theory Comput. 2008, 4, 435.

642-10 WebFF Molecular Dynamics (MD) Force-Field Repository: Schema Expansion, Programming and Data Testing
Frederick R. Phelan Jr., 301-975-6761, frederick.phelan [at] nist.gov (frederick[dot]phelan[at]nist[dot]gov)
WebFF is an online repository for Molecular Dynamics (MD) force-field (FF) data designed to support the Materials Genome Initiative (MGI) for organic compounds and related soft materials. The repository is built using the NIST Configurable Data Curation System (CDCS) which supports ontology-based database descriptions using XML schemas. The student will assist on working with data partners to expand our XML schema description to new atom types, curate data, and develop custom exporters to support specific applications and expand force-field assignment tools. Specifically, we will with the Multiscale Polymer Toolkit (MuPT) group to enable force-field data to be easily interchanged between these efforts. In advance of the project, other developers will also be approached for cooperation.  [In-person or virtual opportunity]

642-11 Probing Adsorption at Metal Step Edges
Michael Woodcox, michael.woodcox [at] nist.gov (michael[dot]woodcox[at]nist[dot]gov)
The student will perform density functional theory calculations to uncover the mechanisms behind growth, migration, and dissolution of steps on metal surfaces. The work will be computational with no laboratory component. [Virtual opportunity]

642-12 Computational Study of Electrodeposition
Kathleen Schwarz, 301-975-2821, kathleen.schwarz [at] nist.gov (kathleen[dot]schwarz[at]nist[dot]gov)
Integrated circuits rely on copper interconnects to connect circuit elements.  Electrochemical deposition can be used to form these copper interconnects. Controlling electrochemical deposition requires an understanding of the formation and dissolution of steps on metal surfaces.  In this project, the student will learn how to perform density functional theory calculations on charged metal surfaces, with the goal of identifying long-range interactions between steps and defects on the surface.  The work will be computational with potentially an optional minor laboratory component (e.g., nanoparticle synthesis for spectroscopy work).  If laboratory work is performed, safety training will need to be completed prior to initiation of laboratory component. [In-person opportunity]

Materials Measurement Science Division (Div 643)- 2025 project

643-06 Computational X-ray Spectroscopy for Catalysis
John Vinson, 301-975-4336, john.vinson [at] nist.gov (john[dot]vinson[at]nist[dot]gov)
Catalysts are used to manufacture most chemical products, helping both to lower the energy cost of a reaction as well as selecting for desired end products. The design and optimization of catalysts is hampered by the difficulty of measuring chemical reactions under realistic conditions, often high temperature and pressure. While X-ray measurements are compatible with these reaction conditions, they require support from calculations and modeling to understand and interpret the results. In this project, the student will learn how to carry out density-functional theory calculations to describe a system’s electronic structure and spectroscopy calculations to describe the interaction with X-rays, and they will gain experience running calculations on high-performance computer clusters. The goal of the project is to better understand heterogenous catalysts by describing how the electronic structure of small molecules changes with adsorption onto a catalyst surface and how these changes can be understood through X-ray measurements. The work will be computational with no laboratory component. [In-person opportunity]

Biosystems and Biomaterials Division (Div 644) - 2024 project shown as example

Evaluate Chat-GPT Performance Using Detect, Measure and Verify Method
Talapady Bhat, bhat [at] nist.gov (bhat[at]nist[dot]gov)
Chat-GPT, LLM and AI has become a common household topic of interest to everyone. During the year 2023 my SURF and SHIP students worked to evaluate the performance of Chat-GPT to approximately 500 Covid-19 related question and answers. This study revealed that Chat-GPT answers are just about 30% accurate to the question. A primary reason for this poor performance of Chat-HPT is poor quality of the reference documents available for LLM to generate accurate answers. In 2024 students will develop tools to further evaluate Chat-GPT and LLM and suggest mitigative measures to alter reference documents. [Virtual opportunity]

Biomolecular Measurement Division (Div 645) - 2024 project shown as example

Structure Refinement of Nucleic Acids
Christina Bergonzo, 240-314-6333, christina.bergonzo [at] nist.gov (christina[dot]bergonzo[at]nist[dot]gov)
Structures of nucleic acids that have been solved by Nuclear Magnetic Resonance (NMR) spectroscopy are derived from a combination of data collected experimentally, and the computational tools used to combine all of those experimental observables into a group of structures, called an ensemble. Students will work on creating best practices guidelines for NMR refinement protocols using molecular dynamics (MD) simulations, which have advanced electrostatic and solvent descriptions, to refine a sample nucleic acid. Students will learn the basics of solution state molecular dynamics simulations, including structural biology of nucleic acids and analysis of NMR data and trajectory data. They will contribute to the authorship of publicly available NMR refinement tutorials. [Virtual opportunity]

Chemical Sciences Division (Div 646) - 2025 project

646-02 Evaluating Molecular Models of Carbon Dioxide and Water
Alexandros Chremos, 301-975-5891, alexandros.chremos [at] nist.gov (alexandros[dot]chremos[at]nist[dot]gov)
The capture and utilization of carbon dioxide (CO2) from emissions increasingly becomes vital to the circular economy. The development of technologies in this direction relies on robust theoretical models that accurately predict the thermodynamic behavior of CO2 and other participating components, such as water, over a wide range of temperatures and pressures as well as compositions. We will focus on the phase behavior of CO2 as a single component and in aqueous mixtures. We will utilize an equation of state (SAFT) to evaluate its predictions, and SAFT models will be evaluated in molecular simulations to describe the thermodynamic behavior of these systems. [In-person opportunity]

MATERIALS SCIENCE: This concentration focuses on synthesis, measurements, and theory of innovative materials and devices. Note: This concentration includes projects from the NCNR. Additionally, a limited number of projects are available at the NCNR for students with interest in nuclear engineering and/or reactor operations.

NIST Center for Neutron Research - Neutron Condensed Matter Science Group (Div 610.02) - 2025 projects

610.02-01 Quantum Materials: Synthesis and Property Characterization
Nicholas Butch, nicholas.butch [at] nist.gov (nicholas[dot]butch[at]nist[dot]gov)
The applicant will participate in synthesis of quantum materials with a choice of materials that host novel magnetism, superconductivity or other electronic phases, which are being studied by the Butch group at the NCNR-affiliated Quantum Materials Center at the University of Maryland. Applicant will utilize x-ray diffraction, magnetometry, and other electronic property probes to characterize the structure and properties of synthesized samples. If available, NCNR facilities will also be utilized. Applicant will learn data analysis and interpretation. This project will involve work both at NIST, in Gaithersburg, and at the University of Maryland in College Park. [In-person opportunity]

610.02-02 Voltage-driven Control of Giant Magnetoresistance for Magnetoresistive Random Access Memories (MRAM)
Shane Lindemann, sml8 [at] nist.gov (sml8[at]nist[dot]gov)
Voltage control of magnetism is a promising candidate for use in the next generation of Magnetoresistive Random Access Memories (MRAM).  Here we aim to utilize strain-mediated coupling by fabricating magnetic thin films on substrates possessing piezoelectricity, a material property that allows electrical energy to be converted into strain.  By applying an electric field across the substrate, the generated strain is transferred to the thin films resulting in changes in magnetism that can alter the electrical resistance of spin valves that consist of alternating layers of magnetic/nonmagnetic films.  This mechanism is expected to provide higher storage densities, faster writing times, and orders of magnitude lower power consumption than today’s magnetic-field-write RAM. [In-person opportunity]

610.02-06 Applications of AI to Neutron Diffraction
William Ratcliff, ylem [at] nist.gov (ylem[at]nist[dot]gov)
Determining the structure of a material is the first step toward understanding and controlling its properties.  The best way to do this is through diffraction experiments.  In this project, the student will work to accelerate the analysis of diffraction data through the use of novel artificial intelligence models. [In-person opportunity]

610.02-07 Using Neutron Scattering to Reveal the Magnetic Ordering of a Topological Magnet
Julie Borchers and Jonathan Gaudet, %20julie.borchers [at] nist.gov ( julie[dot]borchers[at]nist[dot]gov) and jonathan.gaudet [at] nist.gov (jonathan[dot]gaudet[at]nist[dot]gov)
Topological magnets are materials whose properties are dictated by the topology of their electronic wavefunctions, which are highly intertwined with their spin structure and spin excitations. The goal of this project is to carefully determine the magnetic order of the topological magnet NdSb, which is currently highly debated amongst various experimental groups. To do so, the SURF student will get familiar with the different neutron scattering techniques and use triple-axis neutron spectroscopy data to determine the appropriate spin structure for NdSb. [In-person opportunity]

Materials Science and Engineering Division (Div 642) - 2025 projects

642-03 Exploring Layer Interactions Between High Performance Photoresists and Underlayers for Extreme UV Photolithography
Matthew Wade, 301-975-6783, matthew.wade [at] nist.gov (matthew[dot]wade[at]nist[dot]gov)
(CHIPS) Advanced electronics chip manufacturing requires new materials to print ever smaller features using extreme UV photolithography. Consisting of complex, multi-component polymer films, these photoresists are used to convert light projected through a pattern mask into physical features. In this project, the student will evaluate how the components of a photoresist disperse and interact with films underneath it. The student will prepare multi-layer films and characterize the depth distribution of components through ellipsometry and reflectometry. Through this work, the student will develop an understanding of the interactions between films, the mechanisms that can lead to the diffusion of critical components, and, in turn, how these behaviors can impact the performance of the photoresist. [In-person opportunity]

642-07 Measuring Residual Stress Caused by Moisture and Temperature in Semiconductor Packaging Resins
Stian Romberg and Polette Centellas, 301-975-5241, stian.romberg [at] nist.gov (stian[dot]romberg[at]nist[dot]gov) and polette.centellas [at] nist.gov (polette[dot]centellas[at]nist[dot]gov )
(CHIPS) Epoxy resins are frequently used to package and protect connections in semiconductor packages and electronics. Humidity and thermal cycles (i.e., hygrothermal stressors) cause these resins to change dimensions, creating residual stress and warpage in the disparate components included in semiconductor packages. Understanding the hygrothermal expansion coefficient is key to predicting the magnitude of warpage experienced by a package. Therefore, the SURF student will measure moisture uptake and dimensional changes at different relative humidities and temperatures. Through this experimental project, the student will gain experience working with epoxy composite resins used in semiconductors, and potentially writing code to analyze data. The student will be trained to handle the epoxy safely and will be presented with the relevant hazard reviews for conducting work in the lab. Space and equipment will be prepared to ensure a safe working environment. [In-person opportunity]

642-13 Extracting Usable Fluorescence Signal from Time-gated Raman Data: Can It Be done?
Julie Rieland, 301-975-2616, julie.rieland [at] nist.gov (julie[dot]rieland[at]nist[dot]gov)
Fluorescence is a major interference in Raman scattering measurements, but in theory, when isolated, it can also tell us useful information about a studied sample. Using computational methods and a new Raman instrument configuration, we can now deconvolute the fluorescence and Raman signal, raising the question of if this extracted fluorescence signal is representative of the true fluorescence behavior. Student work will involve collecting Raman measurements of a variety of samples including well characterized fluorescent dyes, processing the data using the deconvolution code written in Python, comparing data collected across different instrumentation, and writing a report of the results. This project will require laser safety training and specific instrument training to work with the Raman instrument. [In-person opportunity]

642-14 Machine Learning-Enhanced Property Prediction from Near-Infrared Spectroscopy Towards Improving Plastic Recycling
Sara Orski, 301-975-4671, sara.orski [at] nist.gov (sara[dot]orski[at]nist[dot]gov)
NIR spectroscopy is the current workhorse for sorting plastic waste in recycling facilities owning to its rapid processing capabilities. However, NIR has limitations, for example, it is unable to differentiate certain subclasses of polyolefins. To address this challenge, our research integrates machine learning with NIR spectroscopy, enabling the successful prediction of physical properties of polyolefins therefore refining the sorting process. To further enhance the applicability of NIR spectroscopy in plastic recycling, we propose to investigate the impact of instrument resolution on NIR spectroscopy data for plastic recycling applications. The student will conduct a direct comparison of NIR data obtained using a Fourier-transform (FT) instrument and a handheld reflectance NIR instrument. The focus will be on analyzing data from commercial polymer pellets, consumer plastic parts, and flake materials.
This study will evaluate how differences in resolution and measurement configurations affect machine learning model accuracy and identify necessary adjustments for applying these models across diverse instrumentation commonly used in secondary recycling and industrial settings. The insights gained will support model modifications to enhance flexibility and applicability.
The student will gain hands-on experience with NIR spectroscopy and data analysis, contributing to research that bridges laboratory advancements with real-world applications in plastic waste sorting and recycling. This project aligns with NIST’s mission to enhance sustainability and supports the development of practical solutions for the circular economy.
Safety considerations for the student will include proper PPE, training on laboratory access and equipment through OSHE modules and hands on training my lab contact/instrument superuser. [In-person opportunity]

642-15 Optimizing Melt Filtration to Control Crystallization in Post-consumer Resin Feedstock
Paul Roberts, 301-975-6590, paul.roberts [at] nist.gov (paul[dot]roberts[at]nist[dot]gov)
Successfully recycling plastics requires removing debris, typically achieved by filtering the molten plastic through a mesh or sieve. However, this processing step may lead to polymer chain scission and flow-induced crystallization, which can result in anisotropic and mechanically inferior products. In this project, we seek to quantify the effects of melt filtration on flow-induced crystallization and chain scission, facilitating industrial adoption of recycled plastics. We will use capillary rheology to impart and measure the effect of industrial processing conditions on high-density polyethylene (HDPE) and recycled HDPE. Additionally, we will create and test our own HDPE formulations to isolate the contributions of additives present in recycled HDPE. [In-person opportunity]

642-16 Building a Data Driven Framework to Predict and Bridge Multiscale Mechanical Phenomenon in Additively Manufactured Components
Dilip K. Banerjee, 301-975-3538, dilip.banerjee [at] nist.gov (dilip[dot]banerjee[at]nist[dot]gov)
The student will primarily work on (a) learning the use of Thermo-Calc and TC-PRISMA software, (b) use finite element simulated thermal data in Thermo-Calc and TC-PRISMA to generate a spatial map of phases and mechanical properties, and (c) performing nanoindentation tests in critical regions for model validation. Part (c) will be mainly conducted when the student returns to the university following the SURF project work. Some tests can be conducted at NIST, if needed. [In-person opportunity]

Materials Measurement Science Division (Div 643)  - 2025 projects

643-01 Temperature-Dependence of Microscale Mechanical Properties of Heterogeneous Structures in Advanced Packaging
Yvonne Gerbig, 301-975-6130, yvonne.gerbig [at] nist.gov (yvonne[dot]gerbig[at]nist[dot]gov)
(CHIPS) Managing internal thermomechanical stress is crucial to ensure high performance and reliability of 3D stacked integrated circuits in Advanced Packaging (AP). The semiconductor industry employs finite element (FE)  modeling and simulations to predict the distribution of thermomechanical stresses induced in such heterogenous structures during the manufacturing process. Accurate materials data measured on the length scale and in the temperature range relevant to semiconductor manufacturing are needed as input for design-for manufacturing/reliability FE modeling.
In this SURF project, the student will assist in developing and testing experimental procedures for instrumented indentation to measure mechanical properties (such as elastic modulus, yield stress and creep) as function of temperature on materials and structures relevant to AP. Instrumented indentation (aka nanoindentation) is a widely used technique to characterize and measure the mechanical behavior on the microscale for a broad range of materials. The goal of this project is to generate a first set temperature-dependent mechanical data on a sample structure to be shared as a data publication with the semiconductor manufacturing and research communities.
For this SURF project, we are looking for a student with background in physics, engineering or material science who is interested in gaining or expanding their hands-on experience in planning, conducting, and analyzing experiments in a lab setting. 
The student will be provided with the necessary training, equipment, and environment to conduct their research safely and in adherence to NIST safety rules. [In-person opportunity]

643-02 Lifetime Estimation of Electronic Packaging Materials Through Decomposition Kinetics via Thermogravimetric Analysis
Amanda Forster, 301-975-5632, amanda.forster [at] nist.gov (amanda[dot]forster[at]nist[dot]gov)
(CHIPS) The lifetime prediction of the materials used in advanced packaging has become increasingly important for semiconductor devices operating under high-temperature applications. In this project, the SURF student will evaluate different electronic packaging materials using thermogravimetric analysis (TGA). Both controlled-rate and isothermal experiments and analysis will be used to gain insight into thermal decomposition kinetics. The student will first prepare the materials according to proper cure schedules, then prepare the specimens for testing, complete TGA tests, and analyze the results, including decomposition kinetics modeling based on isoconversional methods and ASTM E1641. Finally, the estimated lifetime versus failure temperature will be predicted for thermal degradation. [In-person opportunity]

643-03 Implementing Open-source Stereo Digital Image Correlation for Epoxy Deformation Measurement
Alexander Landauer, 301-975-8392, alexander.landauer [at] nist.gov (alexander[dot]landauer[at]nist[dot]gov)
(CHIPS) Epoxy resins are critical for assembly of modern semiconductor-based devices. The volume change during cure (cure shrinkage) and the coefficient of thermal expansion (CTE) of these resins are important inputs to optimize designs against known failure modes from residual and thermal stresses. In this project, stereo digital image correlation (DIC) is used to measure shrinkage and CTE of industry-relevant and idealized epoxies. The student will be trained on digital image correlation, and investigate and implement open-source software options for conducting the DIC to improve the openness of the workflow. They will also learn to and conduct experiments on resins and prepare and share data with internal and external collaborators for cross-validation of the methods. The student will be trained to safety operate a paint booth and reflow oven. No hazards beyond normal lab operation or those experienced during activities of daily living are expected. [In-person opportunity]

643-04 X-ray Fluorescence Mapping of Thin Films for Semiconductor Applications
Donald Windover, 301-975-6102, donald.windover [at] nist.gov (donald[dot]windover[at]nist[dot]gov)
(CHIPS) X-ray fluorescence (XRF) characterization allows us to determine the chemical composition of bulk materials and to estimate the amount of an element within a thin film.  This method provides an accurate method to determine thin film uniformity and to look for contamination.  Measurements of multiple areas across a wafer will allow for mapping of thin film deposition uniformity and is the first step in developing thin film standards for numerous other characterization methods. [In-person opportunity]

Chemical Sciences Division (Div 646) - 2025 project

644-04 SRD and SRM webpages AI Ready
Talapady Bhat, 301-975-5448, talapady.bhat [at] nist.gov (talapady[dot]bhat[at]nist[dot]gov)
The student will assess Genini AI's responses to topics relevant to documents, evaluating their quality and accuracy. Based on this evaluation, they will identify potential shortcomings or areas for improvement in the AI-generated content and formulate hypotheses to address these issues. The student will then implement changes to the webpages, such as refining prompts, adjusting AI parameters, or modifying the content itself. After making these modifications, they will re-evaluate the AI-generated content to assess the impact of the changes. This iterative process will help to continuously improve the AI readiness of SRD and SRM webpages. [Virtual opportunity]

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Physical Measurement Laboratory (PML)

The PML sets the definitive U.S. standards for nearly every kind of measurement in modern life, sometimes across more than 20 orders of magnitude. PML is a world leader in the science of physical measurement, devising procedures and tools that make continual progress possible. Learn more about PML.

Contacts

Physics
Uwe Arp, (301) 975-3233, uwe.arp [at] nist.gov (uwe[dot]arp[at]nist[dot]gov)
Maritoni Litorja, (301) 975-8095, maritoni.litorja [at] nist.gov (maritoni[dot]litorja[at]nist[dot]gov)

PML Research Opportunities

Office of Weights and Measures (Div 680) - 2025 projects

680-1 Metric System Learning Resources
Elizabeth Benham, (301) 975-3690, elizabeth.benham [at] nist.gov (elizabeth[dot]benham[at]nist[dot]gov)
The candidate will analyze, design, develop, and implement website content, classroom activities, games, illustrations, posters, and training videos that provide K-12 teachers and student learners opportunities to gain experience using the International System of Units (SI, commonly known as the metric system). The candidate will collaborate with their NIST mentor to co-author and publish new education resources that integrate STEM concepts and real-world measurement applications. Projects will apply the Next Generation Science Standards crosscutting concepts of scale, proportion, and quantity. This research supports NIST efforts to increase understanding of the SI through educational information and guidance in government publications. [In-person opportunity]

680-2  Legal Metrology Calibration Research
Micheal Hicks, 301-975-4615, Micheal.Hicks [at] nist.gov (micheal[dot]hicks[at]nist[dot]gov)
Candidate will research, design and develop Office of Weights and Measures (OWM) Laboratory Metrology Program Proficiency Testing (PT) tools (e.g., statistical analysis, process management, artifact inventory management and tracking, and quality system improvements) to support calibration laboratory Recognition and Accreditation requirements. Candidate will collaborate to develop measurement science training and professional development resources. [In-person or virtual opportunity]

Microsystems and Nanotechnology Division (Div 681)  - 2025 projects

681-1 Robust and Efficient On-chip Wavelength Conversion for Hybrid Quantum Networks
Jordan Stone, 301-975-4414, jordan.stone [at] nist.gov (jordan[dot]stone[at]nist[dot]gov)
A fully-fledged quantum network must connect numerous quantum “nodes” that serve disparate purposes and therefore use different wavelengths of light, making communication between them difficult. One solution to this “rainbow problem” is to situate wavelength converters, which harness optical nonlinearity to induce effective photon-photon collisions that produce the desired color, between nodes. However, real devices often suffer from poor conversion efficiency and/or high power consumption, among other drawbacks. Another problem is ensuring that devices faithfully produce the wavelengths for which they were designed (i.e., wavelength accuracy). Recently, experiments with on-chip silicon nitride (SiN) microresonators have separately demonstrated > 90 % conversion efficiencies and wavelength accuracy. In this project, we will develop SiN microresonators that combine these key advantages and evaluate their suitability for quantum networks. The student will characterize SiN microresonators using laser spectroscopy, and they will also assist in wavelength conversion experiments. [In-person opportunity]

681-2 Microfluidic Cytometry
Gregory Cooksey, 301-975-5529, gcooksey [at] nist.gov (gcooksey[at]nist[dot]gov)
This project supports the development of a microfluidic cytometer that repeats measurements of single objects in flow.  These measurements enable estimation of per-object uncertainty, which facilitates optimization of device performance and leads to better classification of sample composition.  The student will learn how to design, make, and use microfluidic devices with integrated optical components. The student will explore measurement of object size and shape and work on metrics to improve counting and classification. [In-person or virtual opportunity]

681-3 Bioelectronic Sensors for Tissue/Organ-on-a-Chip Systems
Darwin Reyes-Hernandez, 301-975-5466, darwin.reyes [at] nist.gov (darwin[dot]reyes[at]nist[dot]gov)
The use of tissue/organ-on-a-chip systems is limited to endpoint and destructive measurements. Integration of electronic elements in these systems have proven to be a huge challenge and only a few demonstrations have been shown. Contrary to endpoint biochemical methods, continuous electronic monitoring of cellular behavior provides an approach for real-time and non-destructive methods that support acute and chronic studies within the same assay. Therefore, we are developing microfluidic-based tissue/organ-on-a-chip devices with embedded electronic elements. Our goal is to fabricate systems and demonstrate their efficacy in delivering real-time, continuous monitoring of cellular responses under stress and disease conditions. [In-person opportunity]

681-4 Meta-optics Enhanced Nanoplasmonic Raman Imaging System
Henri Lezec, 301-975-8612, hlezec [at] nist.gov (hlezec[at]nist[dot]gov)
This project focuses on enhancing Raman imaging for biosensing applications by integrating nanoplasmonic metasurfaces. These engineered surfaces manipulate light at the nanoscale to amplify Raman signals, which are used to identify and analyze molecules. The student will use Finite-Difference Time-Domain (FDTD) simulations to optimize the design of these metasurfaces, maximizing their ability to enhance light coupling and spectral dispersion. The project has two main components. First, the student will optimize plasmonic nanoantenna arrays (NLPNAs) for surface-enhanced Raman spectroscopy (SERS). The goal is to achieve a high signal enhancement factor (EF > 10⁷) in the near-infrared (NIR) region (700-900 nm) to improve the detection of bacterial biofilms in skin wound models. This NIR range minimizes interference from tissue autofluorescence, leading to more sensitive detection. The student will then help fabricate and characterize these devices using advanced techniques like physical vapor deposition and scanning electron microscopy. Second, the student will contribute to the development of meta-optics for the Raman imaging system. This involves designing metasurfaces that enhance both spatial resolution and spectral dispersion. A key focus will be optimizing the in-coupling optics for efficient NIR laser excitation, further improving the system's sensitivity. This project offers a comprehensive introduction to nanophotonics. The student will gain hands-on experience with advanced simulation tools, nanofabrication techniques, and Raman spectroscopy. Working closely with the research team, the student will learn how simulation results guide the design and fabrication of nanoplasmonic and meta-optic devices for cutting-edge biosensing applications. [In-person opportunity]

Radiation Physics Division (Div 682) - 2025 projects

682-1 Thermal Modeling of Cryogenic Radiation Detectors
Max Carlson, 301-975-5608, max.carlson [at] nist.gov (max[dot]carlson[at]nist[dot]gov)
The NIST True Bq project aims to create a system that can detect and quantify virtually any radioactive material. To do this, True Bq uses cryogenic transition edge sensors (TES) for decay energy spectrometry (DES). The TES operates at a temperature of 0.1 K and directly measures sub-pJ thermal energy emitted by the radioactive decay of a single atom. We are looking to improve computational models of the heat flows in the TES, which include non-linear temperature dependence at mK temperatures. This project focuses mainly on computer modelling, with the opportunity for lab work as well. The resultant model will be used to optimize the next-generation of cryogenic sensors for applications such as nuclear medicine, environmental monitoring, and nuclear security. Lab work may include installing and verifying performance of TES chips in our He-3 dilution refrigerator. [In-person opportunity]

682-2 Modeling Charged Particle Thermal Kinetic Inductance Detector arrays for Nuclear Physics
Tom-Erik Haugen, 301-975-4979, tom-erik.haugen [at] nist.gov (tom-erik[dot]haugen[at]nist[dot]gov)
The neutron physics group is developing Charged Particle Kinetic Inductance Detectors (CP-TKIDs) for beta decay experiments. These are cryogenic sensors with an order of magnitude improved energy resolution compared with existing detectors. Current CP-TKID prototypes have a single pixel, but these detectors will be multiplexed into arrays. The student will work with NIST scientists to model the thermal signal response of the CP-TKIDs when incorporated into an array, and aid in the design of arrays with maximal active area. They may have the opportunity to be involved in the data taking and compare results from their simulations with real data. [In-person opportunity]

682-3 Simulating Spectra for Prompt Gamma Activation Analysis of Solid Materials
Shannon Hoogerheide and Heather Chen-Mayer, 301-975-8582, shannon.hoogerheide [at] nist.gov (shannon[dot]hoogerheide[at]nist[dot]gov) and heather.chen-mayer [at] nist.gov (heather[dot]chen-mayer[at]nist[dot]gov)
(CHIPS) High-purity solid reference and test materials are important in semiconductor chip manufacturing. One method for analyzing these materials is Prompt Gamma Activation Analysis (PGAA), in which a material is irradiated by a neutron beam with the gamma ray emission from neutron capture and de-excitation providing bulk composition analysis. This technique is particularly useful for light elements such as H and B that are widely used in semiconductors but hard to measure with other techniques. The student will use a hardware-based spectrum synthesizer to generate simulated spectra including detector response that will be used to develop data analysis tools as we build the neutron generator PGAA facility. An interested student could also expand this work to Monte Carlo-based simulations. Participation in setting up the facility and/or early measurements may be possible. [In-person opportunity]

Nanoscale Device Characterization Division (Div 683) - 2025 projects

PROJECT WITHDRAWN: 683-1 Numerical Analysis and Modeling of Accumulated Microwave Data on Solder Joint Failures to Extract the Kinetics of Pre-solder Fracture Material Changes

683-2 Temporal Memories in Spiking Neural Networks Emergent from Synchronization Dynamics
Matthew Daniels, 301-975-5121, matthew.daniels [at] nist.gov (matthew[dot]daniels[at]nist[dot]gov)
Initial simulations performed at NIST indicate that depolarization of charge on a neuron can produce a synchronizing effect between arriving neural spikes, which can be used in conjunction with plastic signal delays to retrieve memories of temporally-coded ensembles of neural spikes. This project will scale up simulations of these neural models in Python and investigate the role of lateral inhibition in learning several distinct temporal sequences within a single neural assembly, with applications to neuromorphic and extreme-efficiency computing. [In-person or virtual opportunity]

683-3 Numerical Modeling of Electronic and Spin Transport Properties for Nanoscale Solid-State Lattices and Comparative Analysis Against NISQ Device Simulations
Eric Switzer, 301-975-2596, eric.switzer [at] nist.gov (eric[dot]switzer[at]nist[dot]gov)
We have designed a procedure to study the role of correlations for nanoscale (9 to 12-atom) 2D solid-state device and sensor systems, using noisy intermediate-scale quantum (NISQ) devices, i.e., recent generation quantum computers. In our work, we use an extended Fermi-Hubbard Hamiltonian transformed into the qubit basis to study representative phosphorus-doped silicon-based quantum dot lattices. To establish the range of accuracy of this model using NISQ devices, this project will involve the use of a Fortran software package to calculate several transport properties, and compare against NISQ device results using Qiskit in Python. [In-person opportunity]

683-4 Establishing a Cryogenic Testing Setup for Circuit and Device Characterization
Pragya Shrestha, 301-975-6616, pragya.shrestha [at] nist.gov (pragya[dot]shrestha[at]nist[dot]gov)
This project focuses on establishing a cryogenic measurement system for analyzing the performance of circuits and devices at cryogenic temperatures. The work involves setting up and validating a closed-loop cryogenic setup, assessing its operational capabilities (e.g., frequency range and measurement limitations), and designing auxiliary components to interface fabricated chips with the system. The project will include evaluating input/output behavior of circuits and devices under cryogenic conditions, analyzing performance variations across temperatures and frequencies, and compiling a comprehensive report on system functionality and testing outcomes. The goal is to deliver a fully operational, reliable cryogenic testing setup for characterization of devices and circuits at cryogenic temperatures. [In-person opportunity]

683-5 Measurement and Evaluation of Advanced Semiconductor Materials Using Terahertz Spectroscopy
Edwin Heilweil, 301-975-2370, edwin.heilweil [at] nist.gov (edwin[dot]heilweil[at]nist[dot]gov)
Collection and analysis of THz data from advanced semiconductor materials by the Associate will greatly enhance and expand acquired information towards open publication of a NIST-sponsored THz database.  Directed efforts in this area will be of mutual benefit to enhance the student’s knowledge of physical measurement technologies and THz spectroscopies relevant to Industry.  NIST will benefit by sponsoring a SURF student to help acquire and categorize THz data for publication and database generation. •    The Associate has the requisite background, scientific understanding and exposure to physical measurement methods and fundamental mathematical and computer skills to analyze acquired data for this summer research project. •    Detailed temperature-dependent spectral data acquisition and analyses by the Associate will greatly accelerate project goals by studying and evaluating many more relevant semiconductor materials generated within NIST or from outside sources. [In-person opportunity]

683-6 Modifying the Trion Population to Influence Photocurrent Response in Phototransistors
Emily Bittle, 301-975-6298, emily.bittle [at] nist.gov (emily[dot]bittle[at]nist[dot]gov)
This study will look at the influence of charge density on photocurrent response, focusing on the influence of trion formation. Photocurrent response in materials follows from the formation of an electron-hole pair (the “exciton”) which quickly dissociates into free charge. In some 2D semiconductors, it is speculated that the formation of trions, where an exciton strongly interacts with a free charge, can hinder the mobility of charges causing an observed negative photocurrent. Here, we will investigate the photocurrent at varying bias conditions on a transistor while illuminating the device and compare this to spectroscopic measurements of trion formation to ascertain the origins of negative photocurrent. Student tasks: - electro-optical measurement of transistors  - analysis of data using scientific software - other tasks may include sample fabrication, spectroscopic measurements. [In-person opportunity]

Quantum Measurement Division (Div 684) - 2025 projects

684-1 The Miniature Calculable Capacitor
Gordon Shaw, 301-975-6614, gordon.shaw [at] nist.gov (gordon[dot]shaw[at]nist[dot]gov)
The NIST calculable capacitor is a large precision instrument used as a standard for electrical impedance (capacitance, in particular) for the US. This project will create a miniature version of the calculable capacitor using off-the-shelf components and simple machined parts. It will involve development of a precision electrode alignment strategy, programming of instrumentation to acquire and analyze data, and testing of the assembled and aligned instrument against an existing capacitance standard. [In-person opportunity]

684-2 Experiments with Highly Charged Ions Confined in an Electromagnetic Trap
Joseph Tan, (301) 975-8985,Joseph.tan [at] nist.gov ( joseph[dot]tan[at]nist[dot]gov)
Highly ionized atoms can have long-lived states that are potentially useful for many interesting applications, such as candidates for optical atomic clocks and for determination of fundamental constants. Experiments can utilize an ultra-compact electron beam ion trap (mini-EBIT) or the NIST superconductive EBIT to facilitate the production of such exotic charge states. [In-person or virtual opportunity]

684-3 Nonlinear Optics of Rubidium and Cesium Vapor Cells
Zachary H. Levine, 301-975-5453, zlevine [at] nist.gov (zlevine[at]nist[dot]gov)
Lasers interact with the hyperfine-split levels rubidium atoms.  whose optical response is dispersive, nonlinear, and depends on time and space.  The student will investigate at least one of the following: (a) optical response of an optically thin system considering the atom's motion through the laser beam; (b) what justifies neglecting magnetic sublevels; (c) the response in an optically thick system, including the creation of strong probe-conjugate beams leading to soliton formation.  The methods will be numerical with scientific visualization. [In-person or virtual opportunity]

684-4 Characterization of Electromagnetic Actuators for Tabletop Kibble Balances
Leon Chao, 301-975-4763,%20lsc1 [at] nist.gov ( lsc1[at]nist[dot]gov)
To improve the performance of the current generation tabletop kibble balance at NIST (KIBB-g2), redesigning its electromagnetic actuator is necessary. The redesign will focus on reducing the reluctance force of the actuator and reducing the nonlinearity of the magnetic field gradient. The design phase will use both analytical and simulation tools. Once redesigned, the actuator will be fabricated inhouse and characterized using a flexure-based linear stage with a 10 mm travel. By working on this project, the SURF (Summer Undergraduate Research Fellowship) student would gain experience in electromagnetic design, fabrication, mechatronics implementation and feedback control. [In-person opportunity]

684-5 Building an Electronic NIST Torque Realizer (ENTR) with LEGO Components
Zane Comden, 301-975-2416, zdc [at] nist.gov (zdc[at]nist[dot]gov)
This project focuses on developing a proof-of-concept electronic NIST Torque Realizer (ENTR) capable of realizing up to 3 ozf-in of torque with 0.1% uncertainty, utilizing the Kibble principle. The twist? The ENTR will be built with as many LEGO components as possible, making it a dual-purpose device: a functional metrological tool and a captivating outreach demonstration for students of all ages. Inspired by the successful LEGO Kibble balance, this project aims to merge rigorous scientific methodology with creative, hands-on design. [In-person opportunity]

684-6 High Resolution Spectroscopy for Applications in Astrophysics
Jacob Ward, 301-975-3202,jacob.ward [at] nist.gov ( )jacob.ward [at] nist.gov (jacob[dot]ward[at]nist[dot]gov)
Spectrometers on ground and space based telescopes, such as The Hubble Space Telescope, require laboratory atomic reference data for interpreting the observed spectra of astronomical objects such as stars, quasars, and the Interstellar Medium. The NIST Atomic Spectroscopy Group provides the astronomy community with experimental reference data measured on multiple high-resolution spectrometers. Of the many elements, those with atomic numbers near Iron are of special interest for applications in stellar astrophysics due to their relatively high abundance in stellar objects. The spectrum of three times ionized Nickel (Ni IV) is valuable for investigating the physics of White Dwarf stars and astrophysics applications using White Dwarf stars, such as studying the potential variation of physical constants in high gravitational fields. In this project we will investigate the properties of a Penning Discharge lamp to determine if the source is suitable for producing the Ni IV spectrum. This project will involve extensive laboratory work as well as data analysis. The student will assist in operating the source and measuring the produced spectrum on both grating and Fourier Transform spectrometers. [In-person opportunity]

684-7 Quantum Simulation with Classical Fluid
Ian Spielman, 301-975-8664, ian.spielman [at] nist.gov (ian[dot]spielman[at]nist[dot]gov)
A central feature of physics is that physically different systems are described by the same mathematical equations share the same behavior.  This insight forms the backbone of analogue simulation where an experimenter engineers a laboratory system with the same expected behavior as a more difficult to study system of interest.  In the past 30 years or so this strategy has been employed for quantum systems, where ultracold atoms in optical lattices are the ``quantum simulation'' poster-child.  This SURF project will use a classical fluid, in this case a ferrofluid, to simulate the behavior of a quantum system: an atomic Bose-Einstein condensate.  The SURF student will use off-axis holography to measure the fluid's height profile, which along with the velocity field, can be mapped to the superfluid order parameter of the quantum system. [In-person opportunity]

684-8 Inertial Pico-balance
Gordon Shaw, 301-975-6614, gordon.shaw [at] nist.gov (gordon[dot]shaw[at]nist[dot]gov)
Our group has developed miniature mass sensors for measuring very small quantities of material. The sensors are fused silica glass paralellogram flexures, and operate as inertial balances. Their resonant frequency shifts according to their mass. This project will develop methods for their calibration using a combination of different approaches. The most straightforward will be a small added weight measured on a balance. Time permitting, approaches using electrostatics and photon pressure from a laser will be examined. The ultimate goal is the calibration of the smallest mass ever determined using the International System of Units. More info on the project here: https://www.youtube.com/watch?v=pXoZQsZP2PY  [In-person opportunity]

Sensor Science Division (Div 685) - 2025 projects

685-1 Developing Fast Readout Schemes for Optomechanical Pressure Sensors
Daniel Barker, (301)975-0544, daniel.barker [at] nist.gov (daniel[dot]barker[at]nist[dot]gov)
Engineered optomechanical systems are simple and robust pressure gauges that operate from the high vacuum to atmospheric pressure. Such sensors work by measuring the gas-induced damping of microfabricated mechanical oscillators. Under vacuum conditions, the damping time can be minutes long, limiting the sensor’s precision and its sensitivity to pressure dynamics. In this project, we will implement fast readout schemes based on coherent motion control of our optomechanical sensors. Achieving this goal involves upgrading the sensor’s optical interferometer, setting up fast feedback systems, and extending the sensor control software. By realizing measurements faster than the mechanical damping time, we will remove a significant impediment to adoption of optomechanical pressure sensors beyond the lab. [In-person opportunity]

685-2 Particle Image Velocimetry of Anemometer Blockage in Small Wind Tunnels
Christopher Crowley, 301-975-5950, cjc17 [at] nist.gov (cjc17[at]nist[dot]gov)
The presence of an air speed sensor (anemometer) in a flow alters the airflow profile, with the effect being more pronounced in smaller wind tunnels due to the proximity of the anemometer to the tunnel walls. Many calibration labs use small, benchtop wind tunnels to calibrate anemometers, where this blockage effect can be significant. To investigate this blockage effect, the project will experimentally capture in-plane velocity field measurements using high-speed imaging and Particle Image Velocimetry (PIV). These velocity field measurements will be used with other SI traceable measurement techniques to assess the impact on the anemometer calibration. The goal is to first quantify the extent of the effect and to then develop a procedure that enables calibrators to account for this influence during calibrations. [In-person opportunity]

685-3 Simulating Thermal Effects in Rate-of-Rise Standards for Improved Calibration of Semiconductor Flow Meters
Aaron Johnson, 301-975-5954, anj100 [at] nist.gov (anj100[at]nist[dot]gov)
This project aims to improve the accuracy of low-flow primary standards used in semiconductor applications (<500 cm³/min) by simulating thermal effects in a rate-of-rise (RoR) standard. These standards are essential for calibrating mass flow controllers, which are critical in semiconductor manufacturing. In the RoR method, a constant flow of gas is introduced into an evacuated vessel of known volume. The pressure, along with an assumed temperature, is used to determine the gas density and mass during the filling process. By tracking the change in mass over time, the mass flow rate is calculated. COMSOL Multiphysics will be used to model the gas temperature in several NIST RoR standards and explore how temperature depends on both flow rate and gas species. A key challenge with the RoR method is that gas entering the collection tank compresses the gas already inside, causing a temperature increase during filling. This temperature variation makes accurate temperature measurements difficult and is a major source of uncertainty in low-flow primary standard measurements. By simulating these thermal effects across a range of flow rates and different gas species, this project aims to better understand the temperature variations and their impact on measurement accuracy, ultimately reducing uncertainty in low-flow primary standards. [In-person opportunity]

685-4 Python GUI for Spectral Irradiance Responsivity Transfer Instrument (SIRTI)
Joseph P. Rice, 301-963-7226, jrice [at] nist.gov (jrice[at]nist[dot]gov)
The portable Spectral Irradiance Responsivity Transfer Instrument (SIRTI) is under development at NIST to provide an automated method of quickly calibrating spectrographs. Our immediate application is to calibrate spectrographs used in astronomical observatories as part of a larger project at NIST to calibrate that flux (spectral irradiance) from standard stars to high accuracy for supernovae cosmology. SIRTI consists of a supercontinuum light source fed into a monochromator that automatically sweeps a high density of spectral lines across the entire spectrum, measures the flux and wavelength of each line, and applies this set of lines to a spectrograph, thereby calibrating the spectral responsivity (as a function of wavelength) of the spectrograph. The software to automate control and data acquisition has been written in Python. The student will develop a GUI to operate this software, and use it to automatically calibrate spectrographs. Along the way the student will learn some optics and engage with NIST mentors in a practical astronomy project. [In-person opportunity]

685-5 Exploring Majorana Loss in the Cold Atom Vacuum Standard
Stephen Eckel, 301-975-8571, stephen.eckel [at] nist.gov (stephen[dot]eckel[at]nist[dot]gov)
Over the past several years, NIST’s cold atom vacuum standard has been demonstrated to be both a sensor and a standard for vacuum pressures in the ultra-high (<10^{-6} Pa) vacuum regime.  The CAVS works by laser cooling sensor atoms to less than 1 mK temperatures and placing them into a conservative, magnetic trap.  While in the trap, they undergo collisions with background gases that eject them from the trap.  Counting the remaining sensor atoms in the trap after a certain period of time gives us an excellent measurement on the density of background gas molecules through first-principles quantum scattering calculation.  Three different types of CAVSs have been realized at NIST: a laboratory scale CAVS that can use either Li or Rb as the sensor atoms and a portable version that uses Li as the sensor atoms.  All three show deviations from the expected exponential loss behavior at the lowest pressures we can experimentally realize.  One possible explanation for these deviations is Majorana, or spin flip loss: if an atom gets to close the center of the magnetic trap, it can flip its quantum state from one that stays in the trap to one that is ejected by the trap.  We will explore this effect both experimentally and through simulation with the goal of determining its experimental signatures and accurately quantify it. [In-person opportunity]

685-6 Automated Immersion Robot for Fixed Point Cell Certifications
Richmond Wang, 301-975-6152, richmond.wang [at] nist.gov (richmond[dot]wang[at]nist[dot]gov)
The Standard Platinum Resistance Thermometer Calibration Laboratory (SPRTCL) at NIST is responsible for realizing, maintaining, and disseminating ITS-90 temperatures with uncertainties that are competitive with the best NMIs around the world. In order to achieve and maintain such low uncertainties, the fixed point cells utilized at NIST need to be evaluated regularly to ensure the stability and reliability of the cells. One aspect of the evaluations performed are heat flux and gradient measurements of the SPRTs and furnaces used. This project will expand upon a prototype robot constructed in 2021 intended to perform these measurements. Currently, three separate programs are required for this robot to function properly, with programming in both Python and LabVIEW. In this project, the student will iterate upon the current design and integrate it into the existing measurement programs used in the lab. The student will be able to redesign how the robot functions and interacts with the existing measurement programs as they see fit to automatically perform the necessary measurements for the SPRT heat flux and furnace gradient tests. [In-person opportunity]

685-7 Reducing Uncertainties Through Correlated Measurements
Steven Brown, 301-975-5167,swbrown [at] nist.gov ( )swbrown [at] nist.gov (swbrown[at]nist[dot]gov)
Measurements taken sequentially are uncorrelated, making them susceptible to fluctuations in the source being measured. In contrast, measurements taken at the same time are highly correlated, which serves to reduce the measurement uncertainty. In a spectrometer, light enters through an entrance slit, is dispersed by a grating, and is imaged at an exit slit. A single element detector is used in measure the throughput. The grating is rotated to change the wavelength of the light transmitted through the exit slit of the spectrometer. A spectrograph removes the exit slit of the spectrometer and replaces the detector with a multi-channel detector. For this project, the detector is a 2-d CCD. Using monochromatic light, the entrance slit is imaged onto the spectrograph at a particular column. As the wavelength of the monochromatic light is changed, the image of the entrance slit moves across the detector array. In this way. using a spectrograph enables a full spectrum of the light source being measured to be acquired at the same time.  Typically, the entrance slit is fully illuminated; detector columns are summed for each row (corresponding to different center wavelengths). For this work, we illuminate the entrance slit with different optical fibers; each fiber is imaged onto a subset of the CCD in different rows. Signals are calculated for each input fiber by summing over the spatial subset of the columns corresponding to the fiber image on the array. This work will examine the reduction in the Type A uncertainty in simultaneous measurements using a multiple fiber input into a spectrograph. Indoor and outdoor experiments are planned. The indoor measurements will look at the impact of simultaneous measurements of spectral scattering from a diffuse white surface. This work has implications for potential improvements to the national reference instrument for specular and bidirectional reflectance measurements.  Looking at different surfaces, spatial correlations as well as temporal correlations need to be considered. Outdoor measurements will evaluate the impact of temporally correlated measurements from two diffuse white surfaces as a function of the spatial separation between them. The vicarious calibrations of satellite sensors use reference ground targets such as dry lakebeds and desert sites. The calibration is tied to measurements of the surface reflectance of small spatial regions of these ground targets are taken by reference instruments with sequential measurements of reference diffuse white reflectance panels. The uncertainties in these measurements are on the order of 5 %. The reduction of measurement uncertainties using correlated measurements examined in this work has implications for the measurement protocols of instruments used in the vicarious calibration of satellite sensors. [In-person opportunity]

685-8 Absolute Flux Standards for Astrophysics
Susana Deustua, 301-975-3763, susana.deustua [at] nist.gov (susana[dot]deustua[at]nist[dot]gov)
One of the key challenges in modern astrophysics is the accurate measurement of the irradiance (flux) of the astrophysical sources that are used to investigate fundamental questions in Astronomy:  what is dark energy?  what is the distribution of dark matter? how do stars evolve?  what are the properties of exoplanet host stars?  where are habitable worlds located?  One way to help address these questions is to have accurate flux references. Stable stars are used as flux standards by astronomers.  We are building and characterizing hardware that will be used to provide SI-traceable flux calibration of standard stars.   Our goal is to measure the stellar spectral energy distributions flux standard with uncertainties that are 5 to 10 times smaller than the current values.   This research supports cosmology, exoplanet science and stellar astrophysics.   We are building and characterizing calibration systems and instruments that can be used with astronomical telescopes.  In preparation for deploying our telescope system in Chile, we shall be characterizing the instruments, automating telescope operation, and testing in the lab. [In-person opportunity]

685-9 Machine Learning Framework for Firearm Toolmark Identification
Xiaoyu Zheng, 301-975-4095, alan.zheng [at] nist.gov (alan[dot]zheng[at]nist[dot]gov)
The discipline of firearm and toolmark analysis seeks to determine whether two bullets or cartridge cases originated from the same firearm. This is accomplished through comparing the toolmarks on the samples imparted by the firearm. The analysis is performed by highly trained firearm examiners utilizing a comparison microscope to render a subjective conclusion of identification, inconclusive, and exclusion. NIST has developed algorithms which can assign an objective score to the degree of similarity between two firearm toolmark datasets. With the explosion in Artificial Intelligence (AI) and Machine Learning (ML), there is a unique opportunity to apply it to firearm and toolmark analysis. The NIST Ballistics Toolmark Research Database (NBTRD) contains thousands of ground truth measurements of firearm and toolmark samples. Utilizing the NBTRD, the SURF student will train an AI/ML framework by feeding it known matching and known non-matching pairs of datasets. Once trained, a validation dataset of questioned items vs known samples will be used to test the performance of the framework at correctly identifying the matching pairs and excluding the non-matching pairs. Previous applied experience in AI/ML is a requirement for this project. [In-person opportunity]

685-10 Sub-Doppler Cooling of MgF
Stephen Eckel, 301-975-2185, eric.norrgard [at] nist.gov (eric[dot]norrgard[at]nist[dot]gov)
Our lab is focused on laser cooling and trapping large numbers of MgF molecules for metrology and quantum applications. While MgF is quite amenable to laser cooling, it will require sub-Doppler cooling in order to achieve sufficiently low temperatures required for metrology and quantum applications. Sub-Doppler cooling has two special requirements: the correct laser detunings and zero magnetic field. On the other hand, the pre-cooled MgF will be trapped in a magneto-optical trap (MOT), which uses a large spherical quadrupole field. Thus, we need to rapidly switch off the magnetic field used by the MOT. For this, we need to design an insulated-gate bipolar transistor (IGBT) switch capable of handling the roughly 100 A of current used in the magnetic field. After design, testing, and implementation, we will attempt to demonstrate sub-Doppler cooling of MgF molecules already pre-cooled and trapped within a MOT. [In-person opportunity]

685-11 Characterizing and Development of a High-speed Arduino Servo for Laser Stabilization
Kevin Douglass, 301-975-6489, kevin.douglass [at] nist.gov (kevin[dot]douglass[at]nist[dot]gov)
This project is part of a larger NIST project to measure and disseminate the Pascal via optical refractive index of gas.  The refractive index can be used to measure gas pressure at a given temperature using well determined refractive index and density virial coefficients.  Pressure measurements are realized using a specially designed dual optical cavity system made from of a single piece of dimensionally stable material.  A laser is stabilized to each cavity using a servo controller, while the difference frequency between the lasers is measured.  A direct measurement of refractive index is obtained by counting the difference frequency at vacuum and at pressure.  The laser servo controller is a critical and expensive component.  This project aims to replace a high-cost servo with a lower cost high-speed Arduino servo.   Part of this project will include laser locking a narrow linewidth laser to stable a cavity, laser alignment, coding to modify existing C++ code to test the servo under various conditions, and analysis of the locked system. [In-person opportunity]

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Special Projects

Public Affairs Office (PAO) (Div 107) - 2025 Project

PAO provides communications support to help NIST share its research results, services, and programs; to assist policymakers in learning about NIST's role and activities; and to advise and assist NIST managers on public affairs and policy strategies. Learn more about PAO.

107-1 Science Writing
Ben Stein and Robin Materese, 301-975-2763 and 301-975-4158, benjamin.stein [at] nist.gov (benjamin[dot]stein[at]nist[dot]gov) and robin.materese [at] nist.gov (robin[dot]materese[at]nist[dot]gov)
From AI to biological drugs, chip manufacturing to emergency communications, and disaster resilience to quantum computing, NIST conducts research in a broad range of topics that are critically important to people and society. NIST’s Public Affairs Office communicates our research and other activities to a broad audience through news articles, social media, videos, website explainers, and many other formats. If you enjoy sharing your knowledge about science with nonscientist friends and family, have written about science for your school newspaper, or taken a class in science writing, this could be the perfect fit for you. The selected student will gain experience in writing profiles, science explainers, news articles, and other NIST web content for the general public. You will collaborate with communications professionals in social media, graphic design, and video production. You will learn interviewing skills, participate in brainstorming sessions, accompany our video crews for film shoots in our research labs, and gain valuable experience writing published pieces under the supervision of NIST’s science editors.

How to apply: In the “online questions” (particularly #12-14, 19 and 25) of the SURF application, please indicate your interest in applying to PAO as a science writing intern. Prior coursework in writing, journalism, or other communications fields is desirable but not required. Interested applicants will subsequently be required to provide two writing samples that demonstrate the ability to translate chemistry, engineering, physics, biology, computer science or other science research concepts into journalistic lay language. [In-person opportunity]

Office of the Associate Director for Management Resources (ADMR) (Div 130) - 2025 Project

The ADMR oversees a wide range of institutional support services on behalf of the NIST Director and the organizational units. The ADMR works jointly with the Associate Director for Laboratory Programs and the Associate Director for Innovation and Industry Services to ensure organizational priorities and objectives are in alignment with the NIST mission. In addition, the ADMR also serves as a liaison with Department of Commerce (DOC) leadership on matters pertaining to workforce management, information technology and services, safety and environmental management, facilities maintenance and construction, accounting and finance, acquisitions and grants management, budget formulation, strategic planning, research support services and emergency response. Learn more about ADMR.

130-1 Informed Actionable Decision Making (IADM)
Brian Copello, 303-497-7701, bcopello [at] nist.gov (bcopello[at]nist[dot]gov)
The Informed Actionable Decision-Making program is aimed at facilitating a cultural change at NIST fostering continuous process improvement.  Our objective is to relate multiple business process data sets in an environment that enables the development of products that supply real-time situational awareness, create a culture of shared accountability, and enable managers to make information-based decisions aimed at constant process improvement. The program focuses on creating the IT infrastructure to establish and optimize an Enterprise Data Foundation (EDF) that integrates and relates multiple data sets; coding the transformations and visualizations that convert the data into meaningful information that tie to operational objectives; and developing processes, policy, procedures, and training that enable secure data management and effective application of the resulting information.  Ultimately this program will be applied to all of NIST’s business processes affording customers and managers alike the ability make meaningful data-based decisions aimed at constant improvement at the tactical, operational, and strategic levels.  Participants can expect to be exposed to the practical application of skills relating to data architecture, data base management, statistical analysis, coding (SQL, Python, …), cyber security, and business process lean management. [Virtual opportunity]

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