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)
• Management Resources (MR) & Office of Safety, Health and Environment (OSHE) collaboration

2026 - ReSEARCH OPPOrtunities

Communications Technology Laboratory (CTL)

RF Technology Division (Div 672)

672-1 Laser Interferometer Measurements of Piezoelectric Materials
Mentor(s): Angela Stelson, angela.stelson [at] nist.gov (angela[dot]stelson[at]nist[dot]gov) & Robert Lirette, robert.lirette [at] nist.gov (robert[dot]lirette[at]nist[dot]gov)
(CHIPS) Piezoelectric materials, such as lithium niobate and aluminum nitride, are widely used in today’s communications devices. When designing and testing these devices, precise knowledge of the properties of these materials is essential to industry. At NIST, we are developing new techniques for measuring the piezoelectric and elastic constants of materials using non-contact laser interferometer methods. The selected student will work with their mentor in designing and performing related experiments to measure piezoelectric materials. They will gain hands on experience designing and performing experiments in a professional laboratory setting. The student will be trained on laser safety and general laboratory practices prior to performing any experiments and PPE will be provided as needed.

REQUIRED SKILLS: Engineering or physics related background
PREFERRED SKILLS: Programming experience in MATLAB or Python

672-2 Multisignal, Multiport Calibrations (M2C) for Artificial Intelligence Chips
Mentor(s): Nathan Orloff, nathan.orloff [at] nist.gov (nathan[dot]orloff[at]nist[dot]gov) 
The SURF student will contribute to the development of new multisignal, multiport circuit theory and calibrations artifacts to address the inherent limitations of the state-of-the-art. The specific aims are to (1) develop a new multisignal, multiport circuit theory, (2) design a multisignal, multiport calibration kit, (3) work with test and measurement stakeholders to integrate our theory, and (4) partner with AI chip manufacturers to test NIST’s new multisignal, multiport calibration kits on real AI chips.

672-3 Inverse Problems and Signal Processing for Communications Technology
Mentor(s): Jake Rezac, jacob.rezac [at] nist.gov (jacob[dot]rezac[at]nist[dot]gov) 
This project utilizes applied mathematical theory to address challenges in advanced communications systems, such as 6G cellular deployments. Participants will apply mathematical and computational techniques, including those from signal processing and modern machine learning, to solve problems involving electromagnetic field propagation, high-speed waveform sampling, and uncertainty quantification. This project will involve exploring the theoretical foundations and practical applications of inverse problems and data acquisition, with a focus on translating mathematical concepts into real-world solutions. Successful candidates will have programming experience in Python or MATLAB and a background in linear algebra, differential equations, and probability theory.

Spectrum Technology and Research Division (Div 675)

675-1 Machine Learning-Based Anomaly Detection for Telecommunications Systems
Mentor(s): Jeanne Quimby, jeanne.quimby [at] nist.gov (jeanne[dot]quimby[at]nist[dot]gov) 
This project focuses on developing machine learning-based anomaly detection algorithms for telecommunications systems, supporting both electromagnetic compatibility (EMC) and cybersecurity applications. The student will apply machine learning methods to detect anomalous behavior in data from cellular telecommunications devices, as well as perform literature reviews on existing anomaly detection techniques. Future work may involve advancing the statistical methods and applying them to current and emerging telecommunications technologies. This research supports the NIST Electromagnetic Compatibility (EMC) project, contributing to trusted supply chain research by identifying anomalous behavior in user equipment and base stations. The student’s knowledge in data science and mathematics will accelerate progress in developing robust and effective anomaly detection capabilities.

675-2 MmWave Beamforming Measurement for Spectrum Sharing in Next Generation Communication
Mentor(s): Yao Ma, yao.ma [at] nist.gov (yao[dot]ma[at]nist[dot]gov) 
To explore the feasibility of spectrum sharing among next-generation communication systems in several mmWave bands, we have a research need to design and measure transmission beamforming patterns that can help to control and reduce land-communication interference to other systems (such as satellite receivers). The SURF student is expected to work with their NIST mentor on designing and implementing experiments to transmit 5G communication signals using our existing mmWave phase array units and measure the achieved beamforming pattern and performance. The student will gain experience on spectrum sensing and sharing techniques, programming and testing software defined radio and phase array units, and automated measurement utilizing Python programming. 

REQUIRED SKILLS: Engineering related background. 
PREFERRED SKILLS: Wireless Communication and Programming experience in Python

[Back to top of page]

Information Technology Laboratory (ITL)

Statistical Engineering Division (Div 776)

776-1 Statistical Consulting in Experimental Design, Analysis, and Data Management
Mentor(s): Mary Gregg, mary.gregg [at] nist.gov (mary[dot]gregg[at]nist[dot]gov)
Well-designed experiments provide an efficient way to study complex systems. When planning experiments, it is necessary to consider a) the goal of the experiment, b) the merits and limitations of potential designs, and c) any constraints inherit to the system-under-test. Once experimental data has been collected, appropriate statistical analyses must be performed to extract the desired information. In many cases, more than one methodology may be appropriate, and the analyst must make an informed decision about which to apply. An important last step is ensuring that results are reproducible through the archiving of data and code. Statistical consultants can offer essential support to researchers navigating the experiment design, analysis, and data management process. This project will include participating in all aspects of the statistical consultant’s role in the experimental research process for projects at various stages of development. Specific research questions may be related to molecule dilution in a breath surrogate delivery system and spatial coordinates obtained from dimensional measurement instruments.  

REQUIRED SKILLS: Major in Statistics, or related field, with foundational coursework in experiment design and analysis. Programming experience in R.  
PREFERRED SKILLS: Background knowledge in chemistry or engineering will be helpful but is not a requirement. 

[Back to top of page]

Material Measurement Laboratory (MML)

Applied Chemicals and Materials Division (Div 647)

647-1 Finite Element Analysis of a Thermal Conductivity Acoustic Resonator
Mentor(s): Karim Al-Barghouti, karim.al-barghouti [at] nist.gov (karim[dot]al-barghouti[at]nist[dot]gov) & Mark McLinden, mark.mclinden [at] nist.gov (mark[dot]mclinden[at]nist[dot]gov)
(CHIPS) The project aims on developing a novel thermal conductivity acoustic resonator for the characterization of semiconductor process gases. The student will learn and apply advanced FEA techniques to guide the design of the resonator and its response function. Simulations and theory will be used to develop a working model for extracting the thermal conductivity of gases at high pressures and temperatures. The student will also have access to existing acoustic resonators to help in understanding and developing the necessary model for the thermal conductivity resonator. The in-lab portion of the project will require extensive safety training and familiarization with standard operating procedures. The student will have access to a high-performance computing cluster.

REQUIRED SKILLS: Thermodynamics, Transport Phenomena, coding
PREFERRED SKILLS: FEA simulations, electronics, HPC, numerical methods

647-2 Measurement Techniques for Photopolymer Applications
Mentor(s): Rion Wendland, rion.wendland [at] nist.gov (rion[dot]wendland[at]nist[dot]gov) & Jason Killgore, jason.killgore [at] nist.gov (jason[dot]killgore[at]nist[dot]gov) 
Light-based 3D printing is moving from a quick, prototyping process to a relevant, industrial manufacturing technique. Despite widespread interest, the complexity of photopolymerization continues to hinder progress in the development of transferable, standardized, and appropriate measurement techniques, which are vital to advancing and scaling up the light-based 3D printing industry. In this role, the student will work in the lab on measurement research and analysis, with the primarily goal of understanding and characterizing photorheology, as it relates to light-based 3D printing. An ideal candidate should have interest in chemistry, polymers, measurement science, and/or applications of light-based 3D printing.

647-3 Modeling Allosteric Regulation in ERK2: The Impact of Ligand Binding on Enzyme Dynamics
Mentor(s): Demian Riccardi, demian.riccardi [at] nist.gov (demian[dot]riccardi[at]nist[dot]gov) & Theodore Fobe, theodore.fobe [at] nist.gov (theodore[dot]fobe[at]nist[dot]gov) 
This project investigates allosteric regulation in the MAP kinase ERK2, a critical cancer signaling target whose activity is governed by the highly flexible dynamics of its activation loop. Building on our discovery of multiple "settled states" in the apo-enzyme that persist for over 5 microseconds, the next phase explores how ligand binding shifts these conformational equilibriums to modulate the enzyme's kinetic and structural landscape. Working with researchers at NIST and CU-Boulder, the student will help set up new molecular dynamics simulations and perform initial analysis to identify how diverse ligands influence long-range communication with the catalytic site. The ideal candidate possesses a strong interest in biophysical problems and a desire to use computational tools to investigate the fundamental physics of biological systems.

647-4 Exploring the Capabilities of Recently Released Molecular Simulation Tools to Support Thermophysical Data Evaluation
Mentor(s): Vladimir Diky, vladimir.diky [at] nist.gov (vladimir[dot]diky[at]nist[dot]gov) 
The goal of the project is exploring the capabilities of molecular simulation tools such as ms2 (https://doi.org/10.17632/jwwkwmxht2.1) to contribute to thermophysical data evaluation performed at Thermodynamic Research Center and developing applications based on them. A highly independent work in communication with colleagues and developers of the tools is expected. Knowledge and skills needed are understanding of thermodynamics and experience in chemical informatics with Fortran, Linux, Python, and/or C++.

647-5 Development and Benchmarking of Microstructure Segmentation Software
Mentor(s): Austin Gerlt, austin.gerlt [at] nist.gov (austin[dot]gerlt[at]nist[dot]gov) & Jake Benzing, jake.benzing [at] nist.gov (jake[dot]benzing[at]nist[dot]gov) & Nik Hrabe, nik.hrabe [at] nist.gov (nik[dot]hrabe[at]nist[dot]gov)   
For polycrystalline materials (steels, ceramics, etc.), a useful route for optimizing thermal and mechanical properties is to correlate physical properties with microstructure statistics, then tailor the production process to achieve the desired result. As both additive and forming technologies continue to improve, this is becoming an increasingly effective and reliable way to produce high performance parts. However, our ability to tailor properties is partially dependent on the fidelity of our microstructure analysis tools. Furthermore, since many analysis pipelines include proprietary software, it is often unclear how grain segmentation operations are performed, or to what degree differing pipelines can alter the resulting statistics. To that end, this project will involve the creation of an open-source grain segmentation tool in python, which can be used as a standard reference point. This toolset will then be compared with several industry-standard segmentation tools to determine what biases, if any, are introduced by choice of software, and ideally what metrics are pipeline-agnostic and therefore ideal for mapping structure-property relationships.

[Back to top of page]

Physical Measurement Laboratory (PML)

Applied Physics Division (Div 686)

686-1 Simulation and Measurement of Advanced Spintronic Devices and Materials
Mentor(s): Matthew Pufall, matthew.pufall [at] nist.gov (matthew[dot]pufall[at]nist[dot]gov) & Charles Swindells, charles.swindells [at] nist.gov (charles[dot]swindells[at]nist[dot]gov) 
(CHIPS) Magnetic random-access memory (MRAM) is an emerging spintronic technology slated to take the place of FLASH in many future applications. MRAM uses ultrathin (<1.5 nm) magnetic layers to store information and spin currents to write it. At these length scales, bulk values for critical parameters are not accurate, so we are developing a combination of advanced measurements and simulations to determine parameters necessary for MRAM development. In this project the student will learn advanced magnetic characterization methods (both optical and inductive) and atomistic magnetic simulation methods (employing NIST high performance computing resources) we are developing to determine these critical parameters. The project focus (experiment or sim) is flexible, depending on the student’s interests.

REQUIRED SKILLS: Basic familiarity with Python, Introductory E&M coursework
PREFERRED SKILLS: Introductory solid state physics coursework. Laboratory coursework (experimental safety, setup). Abiding and tenacious curiosity.

686-2 Hardware Development for Quantitative NMR and MRI Biomarkers
Mentor(s): Karl Stupic, karl.stupic [at] nist.gov (karl[dot]stupic[at]nist[dot]gov) 
Magnetic resonance techniques such as nuclear magnetic resonance (NMR) spectroscopy and magnetic resonance imaging (MRI) are powerful tools used both in fundamental research and medical settings. In particular, MRI is widely used in clinical settings to observe soft tissues in the body and when necessary, diagnose and monitor diseases. NIST supports the work of the medical and research community by providing calibrated measurements for reference objects, commonly known as phantoms. These objects and their associated calibrated measurements aid in quality assurance testing, monitoring system performance, and validation of new imaging techniques. 
This research opportunity focuses on developing hardware to enhance the capabilities of the NMR and MRI calibration systems at NIST-Boulder. The participant will gain hands-on experience learning to operate NMR and MRI systems. The participant will then transition into a hardware development and/or automation project based on their interest and background. Potential topics range in areas of interest including radiofrequency coil modeling and engineering, robotic platforms for magnetic field mapping, automated sample handling, sample manipulation for image contrast studies. 

686-3 Low-field MRI Hardware
Mentor(s): Stephen Ogier, stephen.ogier [at] nist.gov (stephen[dot]ogier[at]nist[dot]gov) & Katy Keenan, kathryn.keenan [at] nist.gov (kathryn[dot]keenan[at]nist[dot]gov) 
NIST is constructing MRI systems based on arrays of permanent magnets to explore the development and capabilities of low-field MRI. Low-field MRI has the potential to move MRI out of dedicated imaging suites to point-of-care and low-resource settings, improving accessibility. Moving to low field systems presents the opportunity to innovate the design of major system components. This opportunity involves designing, building, and testing RF and gradient coils as well as other support hardware for low-field MRI systems. We aim to open-source these designs and share them with the low-field community (https://www.opensourceimaging.org/). The ideal candidate has CAD and electrical engineering experience and some familiarity with MRI.

686-4 Diffraction Image Analysis with Artificial Intelligence
Mentor(s): Kris Bertness, kris.bertness [at] nist.gov (kris[dot]bertness[at]nist[dot]gov) & Edwin Supple, edwin.supple [at] nist.gov (edwin[dot]supple[at]nist[dot]gov) 
Semiconductor crystal growth is a complex deposition process in which the operating parameters can drift out of the optimal range.  The process can be monitored with a variety of techniques that produce complex images that currently are evaluated qualitatively by eye.  This project will enhance our understanding of this image data with artificial intelligence methods.  The goal will be to uncover subtle trends and relate these insights to growth system parameters to improve yield and quality of the semiconductor films and nanostructures.  The work will contribute to NIST projects focused on the gallium nitride (GaN) semiconductor material family. GaN first came to prominence by enabling the first bright white, blue, and green LEDs that now dominate the lighting market, and is now leading market growth for power electronics for everything from phone chargers to electric vehicles.  Depending on the interest and skills of the student, the project may address other image data sets used to develop new electron microscopy techniques. Required Skills: Experience in image analysis with artificial intelligence programs.

Quantum Sensors Division (Div 687)

687-1 Improving 3D X-ray Imaging of Integrated Circuits using a Scanning Electron Microscope-based Computed Tomography Tool
Mentor(s): Jordan Fonseca, jordan.fonseca [at] nist.gov (jordan[dot]fonseca[at]nist[dot]gov) & Nathan Nakamura, nathan.nakamura [at] nist.gov (nathan[dot]nakamura[at]nist[dot]gov) 
(CHIPS) X-ray computed tomography (xCT) 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 Sensors Division at NIST has developed a prototype nano-xCT instrument and demonstrated 3D reconstruction of integrated circuits with 160 nm spatial resolution. The next phase of this research will improve the instrument’s spatial resolution, scanning speed, and ability to distinguish between metals in a circuit. As a SURF student, you will work with NIST physicists to operate and optimize a newly acquired scanning electron microscope (SEM) and commercial x-ray detector to perform nano-xCT on integrated circuits. You will determine how changing a wide variety of experimental parameters on the instrument impacts image quality and measurement time. You will learn general x-ray CT principles, data analysis and visualization in Python, and techniques for quantification and analysis of scientific images. 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. You should be interested in learning more about the science of 3D x-ray imaging, semiconductor metrology, and contributing to applied physics research. Laboratory work will require that you complete required safety trainings and adhere to NIST safety guidelines, including training and directives as communicated by your mentors. You will work with lab equipment in a safe, approved configuration under supervision from your mentor.

REQUIRED SKILLS: Interested in learning more about the science of 3D x-ray imaging, semiconductor metrology, and contributing to applied physics research.
PREFERRED SKILLS: Some familiarity with the basics of computer programming (e.g. for loops, data structures, use of functions). Some familiarity with Python will be helpful, but experience in Python is not a prerequisite. 

Time and Frequency Division (Div 688)

688-1 Development and Characterization of Chip-Scale Atomic Devices
Mentor(s): Ying-Ju Wang, ying-ju.wang [at] nist.gov (ying-ju[dot]wang[at]nist[dot]gov) 
The project is to develop highly manufacturable, low power, miniaturized quantum devices including magnetic and electric field sensors by combining precision atomic spectroscopy, silicon micromachining, and photonics. Neutral atom based atomic clocks have demonstrated exceptional stability and served as primary time standards for decades but are typically laboratory size. We use microfabrication processes to confine atoms and probe their spectroscopic properties with lasers in order to leverage their advantage of outstanding precision and sensitivity for measuring electric and/or magnetic fields. The student will work closely with the researchers to develop the technology and will have opportunities to explore skills such as spectroscopic measurement, microfabrication, data acquisition, automation, and/or numerical modeling. 

REQUIRED SKILLS: Engineering or physics related background. 
PREFERRED SKILLS: Laboratory and/or optics experience.

688-2 Nonlinear Photonics for Precision Metrology, Computing, and Quantum Technologies
Mentor(s): Grant Brodnik, grant.brodnik [at] nist.gov (grant[dot]brodnik[at]nist[dot]gov) 
A student on this project will perform experimental research in nonlinear photonics using photonic-chip technology, exploring effects such as second-harmonic generation, optical frequency comb generation, and wavelength conversion. These compact, portable devices exploit strong light–matter interaction and engineered nonlinear optical materials to efficiently create new frequencies and broadband spectra on chip. This student will gain hands-on experience with photonic integrated circuits and their free-space and fiber coupling optics; experimental characterization of laser pulses and visible-to-infrared spectra; numerical modeling of nonlinear optical processes; and practical laboratory skills for building, testing, and iterating on advanced integrated photonic systems.

[Back to top of page]

Offices

Management Resources (MR) (Div 130)

Management Resources (MR) is committed to providing comprehensive institutional support services that enable NIST and its organizational units to achieve their scientific, technological, and operational objectives. Through strategic leadership, innovative resource management, and optimized service delivery, we ensure the timely developments, security, and sustainability of NIST’s facilities, operations, and workforce. Learn more about MR.

Office of Safety, Health, and Environment (OSHE) (Div 150)

The Office of Safety, Health, and Environment (OSHE) protects our people and our community. 
The current goals of OSHE are to:

• Ensure NIST regulatory compliance
• Define and communicate clear SH&E roles, responsibilities and requirements across NIST
• Provide straightforward interpretations of regulations, codes, and standards as they apply to the NIST environment
• Provide services that support regulatory compliance and implementation of SH&E programs across NIST
• Identify sound SH&E practices and promote their consistent use across NIST
• Drive continual improvement of SH&E practices at NIST
• Support the NIST Director in creating and sustaining a culture that values safety
  Learn more about OSHE.

130-1 Visualization of Laboratory Safety Data and Interactable Hardware
Mentor(s): Karl Stupic, karl.stupic [at] nist.gov (karl[dot]stupic[at]nist[dot]gov) 
This project is part of an effort to aggregate and visualize laboratory environment and safety data from various streams into a unified web-based platform for NIST staff. Currently, laboratory occupants must review essential safety data from multiple sources and independently monitor environmental conditions. Consolidating this information into a single accessible web portal for each laboratory space will improve data reviews and encourage a safer working environment. A working prototype platform was developed to demonstrate its potential by linking a subset of key databases. This opportunity will focus on further developing the prototype platform to encompass the NIST-Boulder campus laboratory spaces and begin the foundation for extending to the NIST-Gaithersburg campus. Participants can expect exposure to developing web interfaces with writing code in HTML and JavaScript as well as potentially interfacing with SQL databases. 
Depending on the progression of the project and participant’s background, the next phase of the visualization platform would be to interact with key laboratory hardware. This part of the project would involve data collection as well as actionable commands being sent from the platform to microcontrollers connected to hardware. This would allow for remote monitoring of equipment as well as ability to initiate automated routines from the platform. This portion of the project would bring additional exposure to laboratory hardware as well as python or other code languages for back-end scripting. 

[Back to top of page]