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SURF Program Research Opportunities in Boulder, Colorado

Current research opportunities for the 2025 SURF Boulder program are listed below. More research opportunities will be added during the open application period, and eventually, all 2025 SURF Boulder research opportunities will be posted before the application deadline. You may start your application today, and revise it as needed to change your research opportunity preferences later. All projects depend upon the availability of funds.

Applicants are required to list their top four (4) preferences for research opportunities in the online questions section of the application available on USAJobs.gov by 11:59 pm ET on January 31, 2025.

View past projects:
2024 SURF Abstract Book - in person & virtual projects
2023 SURF Abstract Book - in person & virtual projects
2022 SURF Abstract Book - virtual projects only

2024 Acceptance Rate for SURF Boulder: 8%
(16 students accepted out of 200 complete applications received)

2023 Acceptance Rate for SURF Boulder: 15%
(19 students accepted out of 126 complete applications received)

HOST LABORATORIES AND OFFICES

2025 ReSEARCH OPPOrtunities

Communications Technology Laboratory (CTL)

Public Safety Communications Research (Div 671)

671-1 Science Communications Specialist
Kerianne Gibney, 720-298-1329, 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. [In-person opportunity]

RF Technology Division (Div 672)

672-1 Microwave Electro-mechanics for Quantum Transduction
Jacob Davidson, 303-497-6670, jacob.davidson [at] 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. [In-person opportunity]

672-2 Quantum Optics for Microwave-Optical Transduction and Networking
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. [In-person opportunity]

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Information Technology Laboratory (ITL)

Applied & Computational Mathematics Division (Div 771)

771-1 Geometric Interpretations for Pattern Recognition in Images
Zach Grey, zachary.grey [at] nist.gov (zachary[dot]grey[at]nist[dot]gov)
Imaging is fundamental to human interpretation and measurement. When scientists and engineers begin to study an object or environment, they often begin with a picture/image. And, in that image, they often need to extract and compute statistics of coherent patterns or structures to compare and contrast pairs of pictures. We'll be working in-person with scientific computing and geometric methods applied to broad computer vision challenges such as solar ultraviolet imaging (SUVI) of the sun, electron backscatter diffraction (EBSD) of materials like lithium-ion batteries and steel, as well as x-ray computed tomography (xCT) of silicon chip packages. The position requires a candidate curious to explore some or all of the following topics in applied mathematics: (i) image segmentation and morphology, (ii) abstractions of calculus over curves and surfaces, (iii) topological data analysis, and (iv) implementations/comparisons with generative models. Familiarity with linear algebra and computational geometry is very helpful. Exceptional candidates will also have some experience with introductory real analysis and algebraic topology. Programming experience in Python/Matlab is essential. [In-person opportunity]

Statistical Engineering Division (Div 776) - no 2025 projects at this time

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Material Measurement Laboratory (MML)

Applied Chemicals and Materials Division (Div 647)

647-1 Sustainable Property Prediction Methods for Organic Compounds Based on Machine Learning
Ala Bazyleva and Vladimir Diky, 303-497-5981, ala.bazyleva [at] nist.gov (ala[dot]bazyleva[at]nist[dot]gov)
A lot of research has been recently conducted in the field of machine learning for thermodynamic property prediction for organic compounds, but no products, which can be used by others, are generated in most cases. The reasons seem to split into two categories: not providing all necessary components or lost compatibility with external components, which are quickly being developed or modified. The proposed project would explore the life cycle of a machine-learning based property prediction computer program, either an existing or a newly created one, with the goal to determine how it should be shared and maintained in order to preserve usability and to provide the possibility of further improvements. An ideal candidate should have experience in machine learning and related techniques (such as Python and the necessary components) and be able to work independently finding and acquiring new knowledge if necessary. [In-person opportunity]

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Physical Measurement Laboratory (PML)

Applied Physics Division (Div 686)

686-1 Optical Quantum Dots for Coherent Single Photons
Kevin Silverman, 303-497-7948, kevin.silverman [at] nist.gov (kevin[dot]silverman[at]nist[dot]gov)
Semicoductor quantum dots are currently the best performing single photon sources with efficiencies up to 70% and coherence that lasts over 1000s of emission events. The student will work hands on with these structures peforming precision optical spectroscopy in ultracold environments. [In-person opportunity]

686-2 RF Coil Development for NMR/MRI Quantitative Measurements
Karl Stupic, 303-497-4564, karl.stupic [at] nist.gov
NIST provides calibrated measurements to the MRI community to aid in quantitative imaging protocols. To further this work, NIST is developing a field agile magnetic resonance system to provide measurements for magnetic field strengths from 0.064 T to 7 T. This opportunity will develop radiofrequency (RF) coils at various frequencies to expand NIST’s capabilities. The work will involve designing, simulating, fabricating, and analyzing the RF coils for performance. Coils will be studied in the field agile microimaging system to assess RF homogeneity and results of experimental tests. [In-person opportunity]

Quantum Electromagnetics Division (Div 687) - no 2025 projects at this time

Time and Frequency Division (Div 688)

688-1 Reliability Engineering for the NIST Time Scale
Jeff Sherman, 303-497-3511, jeff.sherman [at] nist.gov (jeff[dot]sherman[at]nist[dot]gov)
The NIST time scale is an ensemble of atomic clocks which produces a realization of Coordinated Universal Time, an official source of time and frequency for the United States. Time signals are distributed via the Internet, radio stations, fiber optics, geostationary satellite transponder, and by common-view with Global Positioning System signals. All of this is operated on a 24/7/365 basis. Robust monitoring and alert systems are continuously improved... and could use your help! [In-person opportunity]

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Contacts

Created September 28, 2009, Updated December 6, 2024