SURF Program Research Opportunities in Gaithersburg, Maryland

host laboratories and Offices

Laboratories - Past projects are posted as examples

• Communications Technology Laboratory (CTL)
• Engineering Laboratory (EL)
• Information Technology Laboratory (ITL)
• Materials Measurement Laboratory (MML) and the NIST Center for Neutron Research (NCNR)
• Physical Measurement Laboratory (PML)

Offices 

• Public Affairs Office (PAO) - 2026 project posted
• Office of the Associate Director for Management Resources (ADMR)
• Technology Partnerships Office (TPO)
• Federal Laboratory Consortium for Technology Transfer (FLC)

PAST PROJECTS

Examples of past projects are shown on this page. Many more are available in the abstract books.
2025 SURF Abstract Book - in person projects only
2024 SURF Abstract Book - in person & virtual projects
2023 SURF Abstract Book - in person & virtual projects

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
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 project shown as example

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] 

Smart Connected Systems Division (Div 674) - 2025 project shown as example

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]

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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.

Contacts
Teresa (Tess) Ginley, Theresa.Ginley [at] nist.gov (theresa[dot]ginley[at]nist[dot]gov)
Kathleen Hoffman, kathleen.hoffman [at] nist.gov (kathleen[dot]hoffman[at]nist[dot]gov)
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 shown as example

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 shown as example

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 shown as example

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 project shown as example

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]

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

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 shown as example

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 shown as example

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 Gemini 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 shown as example

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 project shown as example

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]

Materials Measurement Science Division (Div 643)- 2025 project shown as example

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 shown as example

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 project shown as example

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]

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

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]

Materials Measurement Science Division (Div 643)  - 2025 project shown as example

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]

Chemical Sciences Division (Div 646) - 2025 project shown as example

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 Gemini 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
Uwe Arp, (301) 975-3233, uwe.arp [at] nist.gov (uwe[dot]arp[at]nist[dot]gov)
Christina Hacker, christina.hacker [at] nist.gov (christina[dot]hacker[at]nist[dot]gov) 
Zachary Levine, zlevine [at] nist.gov (zlevine[at]nist[dot]gov) 
Julia Scherschligt, julia.scherschligt [at] nist.gov (julia[dot]scherschligt[at]nist[dot]gov) 

PML Research Opportunities

Office of Weights and Measures (Div 680) - 2025 project shown as example

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 project shown as example

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]

Radiation Physics Division (Div 682) - 2025 project shown as example

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]

Nanoscale Device Characterization Division (Div 683) - 2025 project shown as example

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]

Quantum Measurement Division (Div 684) - 2025 project shown as example

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]

Sensor Science Division (Div 685) - 2025 project shown as example

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]

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Special Projects

Public Affairs Office (PAO) (Div 107) - 2026 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, 301-975-2763, benjamin.stein [at] nist.gov (benjamin[dot]stein[at]nist[dot]gov) 
From quantum computing to biological drugs, and chip manufacturing to disaster resilience, 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, blog posts 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. You will conduct a small research project (on a topic TBD) relating to public communication of NIST’s research. 

How to apply: In the “online questions” of the SURF application, please indicate your interest in applying to PAO as a science writing intern by answering “Yes” to question #13 and mentioning this project in question #18. Prior coursework in writing, journalism, or other communications fields is desirable but not required. Interested students will subsequently be asked 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 TBD

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