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

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Information Technology Laboratory (ITL)

Applied & Computational Mathematics Division (Div 771)

771-1 Geometric Interpretations for Pattern Recognition in Images - 2025 project shown as example
Mentor(s): 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]

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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

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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. 

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. 

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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. 

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