SURF Program Research Opportunities in Boulder, Colorado

HOST LABORATORIES AND OFFICES

• Communications Technology Laboratory (CTL) 
• Information Technology Laboratory (ITL)
• Material Measurement Laboratory (MML)
• Physical Measurement Laboratory (PML)
• Office of the Associate Director for Management Resources (ADMR)

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]

672-3 Measuring Sound Speed Dispersion Relations in Piezoelectric Materials
Robert Lirette, 303-497-6864, robert.lirette [at] nist.gov (robert[dot]lirette[at]nist[dot]gov)
(CHIPS) Modern communications devices contain many piezoelectric materials used in radio frequency filters, acoustic delay lines, and in microelectromechanical systems (MEMS). Knowledge of the speed of sound in these materials is required to accurately predict a materials stress to strain relationship as a function of frequency. The speed of sound can often be frequency dependent and we are working to measure these dependencies using ultrasonic techniques. The selected student will work with their mentor to measure the dispersion relations and elastic constants of piezoelectric materials using pulse and resonant ultrasound spectroscopy techniques. They will gain experience in computational data analysis, automated measurements with instrument control, and hands-on experiment design. [In-person opportunity]

Spectrum Technology and Research Division (Div 675)

675-1 Precision Measurement of Noise in Amplifiers and Transistors
Dazhen Gu, 303-497-3939, dazhen.gu [at] nist.gov (dazhen[dot]gu[at]nist[dot]gov)
(CHIPS) Recent progress in low-noise semiconductor devices has outpaced the metrology for product qualification. To be specific, the existing measurement precision is inadequate to differentiate the performance of radio communication frontend chips in noise-sensitive applications. This led GlobalFoundries, a US-based semiconductor manufacturer, to write an open letter to NIST asking for help on this problem. In order to compete globally, US semiconductor chip makers need high-precision on-wafer measurements of microwave noise parameters. This is the set of metrics used to characterize transistors for highly sensitive receivers, and to prove their performance for integration in commercial products. NIST is in the process of developing a new instrument for noise measurements to address this challenge. [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)
(CHIPS) 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)

776-1 Statistical Comparisons of Likelihood Ratio Systems for Automatic Speaker Recognition
Jack Prothero, 303-497-3843, jack.prothero [at] nist.gov (jack[dot]prothero[at]nist[dot]gov)
State-of-the-art automatic speaker recognition methods rely on likelihood-based statistical inference to draw conclusions. This category of inference itself includes several approaches, and the choice of approach greatly impacts the performance and interpretability of the speaker recognition system. This opportunity will involve training and testing a variety of frequentist and Bayesian models on a benchmark speaker recognition dataset. The results of these experiments will directly inform professional standards development for forensic speaker recognition. An ideal candidate for this opportunity will be familiar with fundamentals of probability and statistics and preferably have experience with statistical programming tools in Python or R. [In-person opportunity]

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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]

647-2 Multiscale Modeling of Condensed-phase Biophysical/Aqueous Systems
Demian Riccardi, 303-497-4648, demian.riccardi [at] nist.gov (demian[dot]riccardi[at]nist[dot]gov)
We use computational methods on multiple scales ranging from high-level quantum chemistry to blazingly fast molecular dynamics simulations on GPUs. We have several ongoing and exciting molecular modeling research projects depending on the interest of the candidate: 1) elucidating the allosteric mechanism in a kinase, 2) evaluating modeling decisions in studies of enzyme catalysis, and 3) investigating ions and molecular ionization in aqueous mixtures. Each of these projects will utilize Python libraries to assist analysis. The ideal candidate will have a strong interest in molecular dynamics simulations and/or biomolecular modeling, completed coursework in chemistry, physics, and biochemistry (do you know the amino acids by name?), and some experience with Python in a Linux environment. [In-person opportunity]

647-3 Computational Fluid Dynamics for Metrology Applications
Andrei Kazakov, 303-497-4898, andrei.kazakov [at] nist.gov (andrei[dot]kazakov[at]nist[dot]gov)
(CHIPS) Modern computational capabilities provide an opportunity to simulate fluid flow experiments used in metrology in much more direct manner using realistic geometries and accurate fluid property models. The project is focused on developing computational fluid dynamics models (mesh/boundary conditions/property models/solver workflow) for practical, state-of-the-art devices used for measurements of fluid thermophysical properties. The candidate is expected to have strong mathematical skills, background in fluid dynamics (solution of the Navier–Stokes equations in particular), and experience with programming /scripting. Knowledge of Linux operating system (command-line/shell) is a plus. [In-person opportunity]

647-4 Scanning Probe Microscopy Techniques for 2D and Wide Band Gap Material Quality
Tom Kolibaba, 303-497-5811, thomas.kolibaba [at] nist.gov (thomas[dot]kolibaba[at]nist[dot]gov)
(CHIPS) Next generation semiconducting materials will rely on two-dimensional (2D) and wide band gap (WBG, e.g. GaN) materials' unique properties. Batch-to-batch variability in these materials has hindered the use of domestically sourced 2D and WBG materials in advanced electronics. This project will work to identify methods to quantify defects in these materials and identify correlating measurands between various scanning probe microscopy techniques that predict the presence and frequency of these defects. Applicants should have completed introductory chemistry and physics. Familiarity with electricity & magnetism and/or solid mechanics is helpful. [In-person opportunity]

647-5 Generation and Quantification of Surrogate Particles for Aerosol Breathalyzer Applications
Veruska Malave and Kavita Jeerage, 303-497-4598, veruska.malave [at] nist.gov (veruska[dot]malave[at]nist[dot]gov)
An aerosol generator can be used to produce a fine mist of polydisperse drug-laden particles at a given flowrate and other test conditions. By means of a particle counter and sizer, these aerosols can be characterized based on particle size and number density in real-time measurements. Drug molecules are found as exhaled breath aerosols, forming a sparse/dilute cloud of polydisperse particles of small mass and size—from micrometer to submicrometer in diameter. The Fluid Characterization Group (647.07) at NIST is building the foundational science to support deployment of roadside cannabis breathalyzer technology.  Some of these commercial breath devices rely on the capture of cannabis particles upon the use of impaction filters –i.e., the inertia of particles drives them to collide with the surface of a filter, which can then be collected. We need to develop a sensitive and specific method to create drug-laden particles as breath surrogates to inject into aerosol breath devices. Thus, we can improve and validate our ongoing detection, quantification, and computational-model methods of cannabis compounds that are exhaled into breath devices. The ideal candidate will have hands-on experience in laboratory procedures and test matrix design and analysis and contribute to group discussions. The selected candidate will operate a particle generator and particle counter and sizer using non-toxic/innocuous substances for the generation of controlled aerosol clouds as a baseline study. [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)
Semiconductor 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 performing 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]

686-3 Extreme Ultraviolet Radiometer Development
Chris Yung, 303-497-6299, christopher.yung [at] nist.gov (christopher[dot]yung[at]nist[dot]gov)
(CHIPS) The most advanced semiconductor chips in the world rely on extreme ultraviolet (EUV) light to pattern features only a few nanometers in dimension. EUV generating sources have been developed that produce light from picowatts to almost a kilowatt of optical power at a wavelength of 13.5 nm. Our group is working to improve the absolute measure of this critical wavelength of light by both improving accuracy and the dynamic range of available radiometers. This project will work to develop the electronics required for control and measurement of an EUV electric substitution bolometer. A successful candidate should possess a basic understanding of DC circuits and basic laboratory electronics including lock-in amplifiers and multimeters. Some programming experience with Python and/or Labview would also be beneficial. [In-person opportunity]

686-4 Developing FIB-TOF Techniques for Nanoscale 3D Characterization
Allison Mis and Alexana Roshko, 303-497-5512, allison.mis [at] nist.gov (allison[dot]mis[at]nist[dot]gov)
(CHIPS) Understanding materials properties at the nanoscale is becoming increasingly important as electronic devices shrink. New approaches to characterizing materials are essential for developing smaller, more efficient electronics. This project is focused on combining time-of-flight (TOF) spectroscopy with the Focused Ion Beam (FIB) scanning electron microscope. The FIB is capable of milling materials with nanometer precision, and TOF spectroscopy allows precise elemental and isotopic identification. Combined, these tools can create a 3D map of composition at the nanoscale, information that is essential for understanding and optimizing devices. The student will help develop best practices for acquiring and analyzing FIB-TOF data in support of NIST semiconductor growth and device fabrication. No prior microscopy experience is expected, but an ideal candidate would have had some exposure to coding in MATLAB or Python and curiosity about spectrometry and/or microscopy. [In-person opportunity]

686-5 Superconducting Microwave Resonators for Quantum Applications
John Teufel, 303-497-5039, john.teufel [at] nist.gov (john[dot]teufel[at]nist[dot]gov)
Superconducting microwave circuits are a leading platform for many quantum applications.  This experimental opportunity will study high quality-factor microwave resonators.  Specifically, the resonators will be designed and simulated with high frequency electromagnetic simulation software. The final resonators will be measured at ultra-low temperatures to compare their frequency and losses with design goals.  This lab experience offers introduction to many aspects of quantum technology, including superconductivity, RF circuits, microwave measurements and cryogenics. [In-person opportunity]

686-6 Magnetic Hydrogel-based Bio-sensing
Mark Ferris and Gary Zabow, 303-497-4835, mark.ferris [at] nist.gov (mark[dot]ferris[at]nist[dot]gov)
The magnetic imaging group has several ongoing magnetic biosensor projects based on magnetic nano- or microparticles and smart (size-changing) hydrogels to develop in-vitro diagnostic devices and NMR/MRI contrast agents and sensors. Depending on interest, the candidate may work on analyte recognition in smart hydrogels, building magnetic-hydrogel composite actuators, or microfabrication. The researcher may gain experience in hydrogels, magnetism, magnetic particles research, sensors, 3D-printing, and/or microfabrication. The ideal candidate will have prior hands-on research experience and have knowledge in polymer chemistry, magnetism, and/or engineering. [In-person opportunity]

Quantum Sensors Division (Div 687)

687-1 Data Visualization and Analysis for X-ray Tomography of Nanoelectronics
Jordan Fonseca and Nathan Nakamura, 303-497-4765, jordan.fonseca [at] nist.gov (jordan[dot]fonseca[at]nist[dot]gov)
(CHIPS) X-ray computed tomography (CT) is a powerful method for the nondestructive inspection of opaque objects, providing 3D images of their internal structure. At the nanoscale, this characterization is useful in the semiconductor industry to analyze circuit failure points and to understand device performance. However, modern nanoelectronics contain features too small and complex to be imaged by current commercial instruments. The Quantum Calorimeters Group at NIST has developed a prototype nano-CT instrument and demonstrated 3D reconstruction of integrated circuits with 160 nm spatial resolution. The next phase of this research will seek to improve the instrument’s spatial resolution, scanning speed, and energy binning capabilities. As a SURF student, you will work with NIST physicists to determine how changing a wide variety of experimental parameters on the instrument can improve these three key performance quantities. You will learn general x-ray CT principles, data analysis and visualization in Python, and techniques for quantification and analysis of scientific images, potentially including image stitching. You should have some familiarity with the basics of computer programming (e.g. for loops, data structures, use of functions), but experience in Python is not a prerequisite. [In-person opportunity]

687-2 Measuring Polarization Response of Superconducting Kinetic Inductance Detectors
Anna Vaskuri and Jason Austermann, 303-497-6334, anna.vaskuri [at] nist.gov (anna[dot]vaskuri[at]nist[dot]gov)
Superconducting kinetic inductance detectors (KIDs) are a powerful new superconducting sensor technology for astronomical imaging and spectroscopy, as well as in applications for cosmology and calorimetry. KIDs are operated near absolute zero temperature and can be made polarization-sensitive. Careful understanding and characterization of the polarization qualities of the sensor is critical to many of the end-use science goals. In this project a new cryogenic detector polarization response measurement and characterization system will be developed. Fully cryogenic measurements permit lower optical loading to KIDs which together with the reduced number of optical components between the source and detector will lead to greater sensitivity, lower noise, and higher overall measurement accuracy. The project combines mechanical and electrical engineering, superconducting detectors, data analysis, and experimental cryogenic measurements. Although not a prerequisite, experience in 3D CAD design, Python, and/or cryogenics would be enabling for the candidate. [In-person opportunity]

687-3 Understanding the Limits of Qubit Coherence: Design of Standard Superconducting Resonator Devices
Corey Rae McRae and Doug Bennett, 303-497-6855, coreyrae.mcrae [at] nist.gov (coreyrae[dot]mcrae[at]nist[dot]gov)
Superconducting qubits are currently performance-limited by dielectric losses, inhibiting seamless growth to larger scale quantum computing systems. Coplanar waveguide resonators made from superconducting materials are commonly used to extract the single-photon, low-temperature dielectric loss tangent of materials used to make superconducting qubits. A standard mask set allows research groups to efficiently make devices that work well for these types of experiments, as well as facilitating 1:1 interlaboratory comparisons across the field. The candidate for this project will perform finite-element-method (FEM) 2D and 3D simulations of coplanar waveguides in order to develop new and updated standard device designs. A strong electromagnetics background is essential for the candidate’s success. Previous FEM simulation or CAD design experience is an asset. [In-person opportunity]

687-4 X-ray metrology for the Semiconductor Industry and CHIPS
Jonathan Dean and Joseph Fowler, 303-497-3990, jonathan.dean [at] nist.gov (jonathan[dot]dean[at]nist[dot]gov)
(CHIPS) X-ray fluorescence spectra is a vital tool for elemental analysis, measuring chemical composition, and probing fundamental physics. Historically, data is lacking in the soft X-ray regime, between 100 eV and 1,000 eV. Moreover, extant data is characterized in an ad hoc manner not suitable for portable standards. In the Quantum Sensors Division at NIST, Boulder, this problem is being addressed with state-of-the-art cryogenic transition edge sensors. Advancing characterizations in this regime would improve the entire supply-chain of semiconductors, from mining and extraction to analyzing completed microchips, a fundamental goal of the CHIPS act. The successful candidate will employ AI and improve upon existing code to recharacterize X-ray data in a self-consistent method. The second goal will be performing theoretical relativistic quantum mechanics calculations with QED corrections in an attempt to model the X-ray spectra from first-principles. By the end of the SURF program, the candidate should have achieved these two goals on the Si K alpha X-ray line, and if time allows, could move on to other spectra. [In-person opportunity]

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]

688-2 Covert Communications Using High-stability Atomic References
Tara Fortier and Nick Nardelli, 303-497-4783, nicholas.nardelli [at] nist.gov (nicholas[dot]nardelli[at]nist[dot]gov)
Covert communication is the art of transmitting messages without revealing that communication is taking place at all. Unlike encrypted communication, which hides the content of the message but not the act of messaging, covert communication conceals both the message itself and the fact that any exchange is happening. In our lab, we harness specialized lasers to interact with atomic clocks, generating some of the world’s lowest-noise electronic signals. These ultra-precise signals serve as ideal radio frequency and optical carriers for covert communications, where decoding the message is impossible without demodulation using an equally high-performance signal source. The proposed project would explore transmission, reception and synthesis of ultra-low noise microwave signals for wireless communications as well as system characterization. The ideal candidate is comfortable working independently in a laboratory environment with a passion for learning and curiosity. [In-person opportunity]

688-3 Software Applications to Support the NIST Time Scale and Time Services
Judah Levine, 303-497-3903,judah.levine [at] nist.gov ( )judah.levine [at] nist.gov (judah[dot]levine[at]nist[dot]gov)
The NIST Time and Frequency Division operates an ensemble of atomic clocks that define the NIST time scale and are the reference for a number of services that distribute this information. The division operates network-based time servers at multiple locations that respond to requests for time in a number of formats. A student working in this program will learn the basics of the atomic clock ensemble and the services that distribute time information in digital formats. An important aspect is monitoring the performance of the service in near real time. The student will develop applications that support these requirements. Some experience with programming in a high level language (such as Python or equivalent) is necessary. In addition, it would be useful if a student had some experience in elementary statistics, analyzing data in an undergraduate laboratory, for example, although this is not a strict requirement. [In-person opportunity]

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Office of Associate Director for Management Resources (Div 130)

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. [In-person or virtual opportunity]