CTL 2025 SURF Program

Internships with the Communications Technology Laboratory

The 2025 SURF program is designed to provide a paid 11-week hands-on research experience in communications technology. CTL's SURF projects offer virtual and in-person opportunities in Boulder, Colorado, and Gaithersburg, Maryland.  Interns are awarded up to a $7,810 stipend, with travel and housing allowances available for those who live outside of the NIST immediate area. Applications are due January 31, 2025. 

With expertise honed over decades of research in antennas and wireless propagation, materials science and electronics testing, as well as communications network protocols and standards, CTL serves as an independent, unbiased arbiter of trusted measurements and standards to government and industry. We focus on developing precision instrumentation and creating test protocols, models, and simulation tools to enable a range of emerging wireless technologies. Click here for additional information on CTL.  

Opportunities located in Boulder:  CLICK HERE TO APPLY 

RF Technology Division (Read More)

Microwave electro-mechanics for Quantum Transduction
Mentor - Jacob Davidson, 303-497-6670, jacob.davidson [at] boulder.nist.gov (jacob[dot]davidson[at]nist[dot]gov)
The scaling challenges preventing superconducting quantum computers from quantum advantage are eased by the creation of a quantum connection between microwaves and light.  The Q-net project seeks to create entangled networks of remote quantum processors, using optically generated states transduced to microwave frequencies. This in-person opportunity involves microwave frequency characterization of electro-mechanical membrane transducer devices fabricated here at NIST. You will gain skills in cryogenics, vacuum systems, quantum optics, and microwave electronics to understand the motion of our devices and/or the quantum states we seek to convert. 

Quantum Optics for Microwave-Optical Transduction and Networking
Mentor - Tasshi Dennis, 303-497-3507, tasshi.dennis [at] nist.gov (tasshi[dot]dennis[at]nist[dot]gov)
Optically networking superconducting quantum computers will allow them to scale and reach unprecedented capacity far beyond classical computers.  The QNet project is creating remote microwave entanglement with optical two-mode squeezed states transmitted through optical fiber to microwave-optical transducer nodes.  This in-person opportunity involves the optical generation and characterization of quantum optical states and their interaction with optical fiber and vibrating membrane transducers micro-fabricated at NIST.  We offer hands-on experience with quantum optics, fiber optics, high-finesse cavities, microwave electronics, and phase-locked control systems to understand the quantum thresholds of these novel systems. 

Science Communications Specialist
Mentor - Kerianne Gibney, 303-497-0000, kerianne.gibney [at] nist.gov (kerianne[dot]gibney[at]nist[dot]gov)
As a Science Communications Specialist for NIST PSCR, the candidate will provide high-level scientific writing and editing for program communications, including articles, website, email marketing, social media, internal/external correspondence, reports and publications, as well as educational materials.  In this role, the Science Communications Specialist will conduct research to gather information, verify facts, and ensure the accuracy and credibility of the content; communicate clearly and effectively with stakeholders and team members to understand requirements, provide updates, and address feedback; and adapt writing style, tone, and voice to suit different audiences, platforms and communication objectives.  The ideal candidate will have an education (in-progress acceptable) or experience in communications, or a related field and strong oral and written communication skills.

Opportunities in Gaithersburg  CLICK HERE TO APPLY 

Wireless Networks Division (Read More)

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

673-2 Deploying Responsive Monitoring Tools to Cloud Native Deployments
Scott Rose, Oliver Borchert, and Doug Montgomery, 301-975-8439, scott.rose [at] nist.gov (scott[dot]rose[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 and 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.

673-3 Performance Analysis and Optimization of ISAC Systems
Jack Chuang, 301-975-4171  jack.chuang [at] nist.gov (jack[dot]chuang[at]nist[dot]gov)   Jian Wang, 301-975-8012  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.

Smart Connected Systems Division (Div. 674) (Read More)

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. 

674-3 Automated Vehicle Comfort Evaluation
Wendy Guo, 301-975-5855, wenqi.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. 

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. 

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.