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SURF Program Research Opportunities in Boulder, Colorado

Research opportunities for the 2024 SURF Boulder program are posted below; more may be added during the open application period. You may start your application today, and revise it as needed to change your research opportunity preferences later.

Applicants are required to list their top six preferences for research opportunities in the online questions section of the application available on by 11:59 pm ET on January 31, 2024.

View past projects:
2023 SURF Abstract Book - in person & virtual projects
2022 SURF Abstract Book - virtual projects only
2021 SURF Abstract Book - virtual projects only

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


2024 ReSEARCH OPPOrtunities

Communications Technology Laboratory (CTL)

RF Technology Division (Div 672)

672-1 Building the Quantum Internet with Microwave-Optical Quantum Transducers
Tasshi Dennis, 303-497-3507, tasshi.dennis [at]
Networking superconducting quantum computers will allow them to scale and reach unprecedented capacity far beyond classical computers. We are creating remote microwave entanglement with optical two-mode squeezed states and microwave-optical transducers. This in-person opportunity involves characterization of a mechanical membrane transducer operated at millikelvin temperatures to understand thresholds for network operation. We offer hands-on experience with quantum optics, microwave electronics, control systems, and cryogenics. [In-person opportunity]

672-2 Developing Acoustic Absorption Metrology in Liquids
Bobby Lirette, 303-497-6864, robert.lirette [at] 
Acoustic absorption spectroscopy is one of the few tools which can probe intermolecular bonds through relaxations. Knowledge about these bonds and relaxations is vital to the chemical manufacturing and pharmaceutical industries. For many chemicals of interest, these relaxations occur in the ultrasonic frequency range. We are working to standardize metrologies for measuring acoustic absorption in various liquids using through-transmission and pulse-echo methods. The student will be working with their mentor to develop new and improve methods for measuring ultrasonic absorption. They will gain experience in computational data analysis, instrument control, and hands-on experiment design. [In-person opportunity]

672-3 Building Toward the Next Generation of Rydberg Atom Sensors
Aly Artusio-Glimpse and Nik Prajapati, 303-497-5661, alexandra.artusio-glimpse [at] (alexandra[dot]artusio-glimpse[at]nist[dot]gov)
Rydberg atom electric field sensors are a highly attractive alternative to classical receivers. These devices are self-calibrated – linking field strength to atomic and fundamental constants of nature, are naturally stable, highly sensitive, and widely tunable over an extraordinary range of frequencies (DC-THz). The Electromagnetic Fields Group has an in-person opportunity for a motivated candidate to support the continued advancement of Rydberg atom sensors. This project will focus on the development of small package Rydberg atom sensors, and the student will utilize their electrical and/or optical engineering skills to do so. A student working on this project will learn basic atomic physics concepts that pertain to the function and operation of these sensors and will get hands on experience with various laser systems and control infrastructure. Some prior hands on experience with electronics or optics is preferred. [In-person opportunity]

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Information Technology Laboratory (ITL)

Statistical Engineering Division (Div 776)

776-1 Method Development and Best Practices for Atomic Clock Metrology
Amanda Koepke, 303-497-4047, aak3 [at] (aak3[at]nist[dot]gov)
NIST researchers recently demonstrated that optical atomic clocks can make frequency ratio measurements with 18-digit accuracies, a significant advance towards the future redefinition the second, with implications for GPS accuracy and dark matter detection. However, these optical clocks are more prone to down time than the microwave clocks used in the past, creating “gappy” data which often strain, or outright violate, the assumptions underlying the statistical models currently used. This project centers around investigating and advancing our new and improved method for the analysis of clock data using a multitaper spectral analysis approach. Some knowledge of spectral analysis would be helpful, but not required; programming experience in R is essential. [In-person opportunity]

776-2 That Doesn't Compute: Examining Phishing Email Click Rates
Julia Sharp, 303-868-0708, [at] ( julia[dot]sharp[at]boulder[dot]nist[dot]gov)
Organizations use the NIST Phish Scale to assess their phishing awareness training program’s effectiveness. The Phish Scale is a categorization of how difficult a phishing email is for humans to detect and allows for understanding the association with a training program’s simulated phishing email exercise click rate. For example, an email that is classified as difficult to detect may have a higher click rate than an email that is less difficult for a person to detect. In this setting, the click rate is defined as the number of people who click on a simulated phishing email divided by the total number of emails sent during the phishing awareness exercise. This definition of the click rate metric may underestimate the actual click rate. For example, the total number of emails sent, comprised of the total number of emails open and unopened, may be overestimated. In this project, we will conduct a literature review on click rates, response rates, and open rates, conduct an analysis or simulation of either an existing or simulated data set to understand how the calculation of the click rate may impact the association with the Phish Scale detection difficulty, and report results through presentation and a brief report. Programming experience in R is essential. [Virtual opportunity]

776-3  Improving Encoding Algorithms for GC-MS Analysis in Forensic Fire Debris Research
Mary Gregg, 303-497-5356, mary.gregg [at] (mary[dot]gregg[at]boulder[dot]nist[dot]gov)
Arson investigations use gas chromatography-mass spectrometry (GC-MS) to analyze fire debris for traces of ignitable liquids. GC-MS analyses produces spectral data in the form of matrices containing ion intensities over time. Quantitative comparison of such data requires an encoding algorithm that removes the time and concentration dependence of the original ion profiles, facilitating the assessment in intra-ion relationships across chromatographic conditions. Prior research has used covariance mapping for the underlying encoding algorithm. However, recent work indicates this algorithm has several notable drawbacks. This project will use simulation to explore alternative computational algorithms for the quantitative comparison of GC-MS data with the goal of identifying the algorithm most suitable for fire debris experimentation. Some background knowledge in chemistry is helpful but not required; programming experience in R is essential. [In-person opportunity]

776-4 Machine Learning Oracle for Parameter Estimation in Statistical Analyses
Lucas Koepke, 303-497-6952, lnk5 [at] (lnk5[at]boulder[dot]nist[dot]gov)
Statistical analyses routinely involve a number of decisions, such as whether or not to preprocess the data, which estimation procedure to use, etc. We have developed a novel approach to making these kinds of decisions by combining deep neural networks with fully synthetic training data into an “oracle” that guides the analyst on which choice will most likely yield less estimation error in the end result. For example, there may be two competing methods to estimate a slope but no specific guidance about which one gives the best estimate for your specific data set; this is where the oracle can help. A proof-of-concept implementation was very successful, correctly choosing the better method over 80% of the time. This project would focus on one or more of the following tasks: quantifying the uncertainty around the oracle decisions, expanding the oracle to new applications, and/or assessing the oracle as a statistical estimator. Programming experience in Python is essential, some familiarity with neural networks and PyTorch would be helpful. [In-person opportunity]

776-5 Simulation and Statistical Modeling of High-cycle Metal Fatigue Data
Lucas Koepke and David Newton, 303-497-6952, lnk5 [at] (lnk5[at]boulder[dot]nist[dot]gov)
High-cycle fatigue is a critical testing scenario in material design to estimate component lifetime and safe use conditions. Statistical analysis of this data is challenging, however. Due to time constraints, testing is typically stopped after some large number of stress cycles, leading to censored data. When failures do occur, the data have a nonlinear relationship with applied stress, and a nonconstant variance. This project aims to develop analysis methodology to address one or more of the following tasks: improving the mixture-regression model currently used to estimate lifetime vs applied stress, simulating realistic data with a variety of behaviors and censoring to formally test various modeling approaches, and/or developing an experiment design to optimize data collection efforts with limited sample size. Essential skills include programming in R and statistical modeling, some familiarity with Bayesian methods would be helpful. [In-person opportunity]

776-6 Development of a Web Application for Computing Limits of Detection
David Newton and Julia Sharp, 303-868-0708, [at] ( julia[dot]sharp[at]boulder[dot]nist[dot]gov)
The number of microbes (e.g., colony-forming units) in a sample are often estimated using serial dilutions. The limit of detection (LOD) is a value that describes the minimum number of microbes in a sample detectable by a given measurement procedure. Sharp, Parker, and Hamilton (2023) proposed a practical method for computing the LOD in serial dilution studies. This practical method uses a negative binomial model, the lowest countable dilution, the volume plated, and the number of independent samples to compute the LOD. In this project, we will review the methodology proposed by Sharp, Parker, and Hamilton (2023) and create a web-based application to illustrate the impacts of changing parameter values for microbiologist practitioners to use to compute the LOD from serial dilution studies. The culmination of this project will be a functional web-based application, a brief report describing the app’s utility, and a presentation. Programming experience in R or Python is essential. Familiarity with a web framework such as Shiny, Dash, or similar is preferred but is not a requirement. [In-person opportunity]

776-7 Statistics of Detector ROC Curves
Michael Frey, 303-497-5690, michael.frey [at] (michael[dot]frey[at]boulder[dot]nist[dot]gov)
Medical diagnostic tests, machine learning classifiers, radar motion detectors, statistical hypothesis tests, and so on  - these are all examples of detection procedures, or detectors. Detectors are characterized by their receiver operating characteristic (ROC) curves, which describe the tradeoff between true detection probability and false alarm probability. The uncertainty associated with an ROC curve is given by a confidence band about the curve. This band is constructed in different ways by researchers, often inconsistently. This project seeks by experiment and analysis to identify the best construction of confidence bands for ROC curves and curve differences, under different standard technical assumptions, using perspectives of computational and Bayesian statistics. Familiarity with R and mathematical statistics is a plus for this project. [In-person opportunity]

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Material Measurement Laboratory (MML)

Applied Chemicals and Materials Division (Div 647)

647-1 Supercritical CO2 Corrosion of Pipelines
May Martin, 303-497-5235, may.martin [at] (may[dot]martin[at]nist[dot]gov)
As carbon capture and sequestration is increasingly viewed as a vital part of a carbon neutral energy system, the transport of carbon from its capture point to the sequestration point needs to be considered.  Steel pipelines are the most efficient means of transporting the CO2.  However, steels are susceptible to corrosion by CO¬2, especially if certain impurities, such as water, are present.  The candidate would have the opportunity to work in a specially design CO2 corrosion facility, setting up the instrumentation and running experiments. [In-person opportunity]

647-2 Multiscale Modeling of Condensed-phase Biophysical/Aqueous Systems
Demian Riccardi and Heidi Klem, 303-497-4648, dmr3 [at] (dmr3[at]boulder[dot]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 Sustainable Property Prediction Methods for Organic Compounds Based on Machine Learning
Ala Bazyleva and Vladimir Diky, 303-497-5981, ala.bazyleva [at] (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]

647-4 Numerical Modeling Applied to Material Characterization Studies
Veruska Malavé, 303-497-4598, veruska.malave [at] (veruska[dot]malave[at]nist[dot]gov)
We are seeking a student with some modeling expertise in at least one of these areas: Computational fluid dynamics (CFD), discrete element method (DEM), and/or finite element analysis (FEA) to assist our experimental scientists in developing measurements and methodologies to study complex matter (gas, liquid, and/or solid materials).  The student candidate will have the opportunity to expand modeling expertise in high-impact projects such as: (a) breath aerosol detection, (b) vapor-liquid equilibrium studies, and (c) mechanical testing for material properties at extreme conditions. [In-person opportunity]

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Physical Measurement Laboratory (PML)

Applied Physics Division (Div 686)

686-1 Tabletop Photon Momentum Amplifier
Kyle Rogers, 303-497-4747, [at] (kyle[dot]rogers[at]nist[dot]gov)
NIST has developed the High Amplification Laser-pressure Optic (HALO) to measure kilowatt-level laser power with very high accuracy using the momentum of light. This system utilizes a suite of high-reflecting mirrors in various planes and a force balance for a power-to-force conversion. We seek to create the second iteration of the system that is a fraction of the size and can provide calibrations to other power meters for commercial measurements. The student will design a new system inspired by the existing one, perform a design review, and purchase and assemble all parts necessary for the system. Final delivery will involve kilowatt-laser testing and measurement against existing standards. [In-person opportunity]

686-2 MRI-readable Radiation Dose Phantoms
Stephen Russek, 303-497-5097, stephen.russek [at] (stephen[dot]russek[at]nist[dot]gov)
This opportunity will develop new radiation-sensitive MRI-readable materials that will go into biomimetic phantoms for radiation dose monitoring. The work will involve fabricating radiation sensitive gels, incorporating into phantoms, radiation exposures, and MRI to assess dose and radiation distributions. NIST MRI and microCT systems will be used extensively and clinical exposures will be done in collaboration with the medical school at the University of Colorado Anschutz. Biomimetic phantoms with realistic bone and tissue structure will be developed to verify radiation planning calculations. [In-person opportunity]

686-3 Low-Field MRI Hardware
Stephen Ogier and Katy Keenan, 303-497-3178, stephen.ogier [at] (stephen[dot]ogier[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. The ideal candidate has CAD and electrical engineering experience and some familiarity with MRI. [In-person opportunity]

686-4 Development of Magnetic Nanomaterials for Applications in Biomedical Imaging
Sam Oberdick and Gary Zabow, 303-497-3054, samuel.oberdick [at] (samuel[dot]oberdick[at]nist[dot]gov)
NIST researchers are exploring magnetic nanomaterials for applications in biomedical imaging. Current directions of research include using nanoparticles as contrast agents for portable low-field magnetic resonance imaging (MRI) and fabrication of magnetic particle assemblies for sensing applications. The candidate will develop skills in biomedical nanotechnology, materials synthesis, and data analysis. The ideal candidate should have a background in physics/materials science and, also, have experience working in a wet chemistry lab environment. [In-person opportunity]

686-5 Magnetic Hydrogel-Based Sensing
Mark Ferris and Gary Zabow, 303-497-4657, gary.zabow [at] (gary[dot]zabow[at]nist[dot]gov)
We have several ongoing biosensor projects based on smart (size-changing) magnetic 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 hydrogels or on building magnetic-hydrogel composite actuators. Candidate may gain experience in hydrogels, magnetism, sensors, 3D-printing, and/or microfabrication.  The ideal candidate will have prior research experience and have knowledge in polymer chemistry, magnetism, and/or engineering. [In-person opportunity]

Quantum Electromagnetics Division (Div 687)

687-1 Monte Carlo Modeling for Nanoscale X-ray Tomography of Nanoelectronics
Nathan Nakamura and Daniel Swetz, 303-497-4765, njn [at] (njn[at]nist[dot]gov)
X-ray computed tomography (CT) is a powerful method for the inspection of integrated circuits, providing 3D images of internal structure. This characterization is critical for failure analysis and understanding 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 imaging of integrated circuits with 160 nm spatial resolution. To push the capabilities of this instrument further, electron-photon transport modeling is required to better understand the relevant physics underlying system operation and guide future system design for tomographic data collection. The candidate will work to develop such a model using existing software, the TOol for PArticle Simulation (TOPAS). The student will learn general x-ray CT principles, electron-photon transport modeling, and techniques for analysis of x-ray spectra. The ideal candidate would have some experience with data analysis in Python. [In-person opportunity]

687-2 Control-analysis Integration for a Superconducting Microwave Resonator Open-source Codebase
Corey Rae McRae, 720-232-1475, coreyrae.mcrae [at] (coreyrae[dot]mcrae[at]nist[dot]gov)
The Boulder Cryogenic Quantum Testbed (BCQT) is a research facility for developing and openly disseminating standard protocols to reproducibly measure the quality factor and performance characteristics of superconducting microwave resonators used in quantum computing circuits. The BCQT also serves as a resource for superconducting quantum device testing in a well-characterized cryogenic environment using traceable, open-source methods developed in broad consultation with companies, universities and NIST. In this project, the student will develop new features for our open-source resonator data acquisition and analysis codebase, including integrating control and analysis to optimize measurement throughput. This codebase is written in Python (using packages like pyVISA, matplotlib, numpy, pandas and scipy), and is made available to users through GitHub, so pre-existing knowledge of Python and Git is essential. This project will be of special interest to students who are interested in quantum computing, and the superconducting quantum computing industry in particular. [In-person opportunity]

687-3 Widely-tuneable Noise Sources for Characterization of Quantum-limited Amplifiers
Jason Asutermann and Logan Howe, 303-497-4785,jea [at] ( jea[at]nist[dot]gov)
Contemporary quantum technologies and fundamental physics experiments rely on amplifying and detecting extremely weak electrical signals. To maximize the quality of measurements in these systems, amplifiers which add the minimum possible noise to the measurement process are of great value. The NIST Quantum Sensors Division is developing superconducting parametric amplifiers operating at the noise limit set by quantum mechanics, which would facilitate quantum computing technology development, astrophysical experiments, and searches for dark matter/energy. A critical step in characterizing these amplifiers involves accurate measurement of the amplifier’s added noise. We are interested in expanding our capabilities in this area by developing a source capable of producing calibrated noise from 0.01 K to 5 K inside a dilution refrigerator. The research fellow will design and build the cryogenic noise source, and then write control and measurement software to use the source to characterize existing amplifier devices. Through the process the student will gain hands-on experience in milliKelvin cryogenics, radio frequency instrumentation and measurement, and computer-aided design (CAD). Prior coding experience (Python or other object-oriented languages) is essential. [In-person opportunity]

687-4 Highly Multiplexed Readout of Kinetic Inductance Detectors for Far Infrared Cameras
Jordan Wheeler, 303-497-5719, jordan.wheeler [at] (jordan[dot]wheeler[at]nist[dot]gov)
There is a growing push to observe the universe at sub-millimeter and far infrared wavelengths. Much of the light at these wavelengths is the starlight that has been absorbed and remitted by cold dust in the early universe and thus contains important information about star formation in early galaxies. However, detector and electronic technologies operating at these wavelengths are still under active development.  These technologies require several key advances to meet the requirements of future space telescopes. One such requirement is to significantly increase the number of detectors that can be electronically monitored and recorded simultaneously.  Our group builds kinetic inductance detectors (KIDs) that are superconducting resonators, with each resonating at a unique radio frequency (RF). Using a software-defined radio, thousands of detectors can be monitored using a single coaxial line. However, the number of detectors that can be measured is limited, in part, by the bandwidth of the RF readout electronics.  One solution is to expand the RF bandwidth beyond the traditional single-octave limit of previous generation readout electronics to allow the resonators to be spaced over a wider range of frequencies. The potential of such multioctave readout requires a measurement of the level of unwanted non-linearity products for each component for a real system. For this project, a student will work to characterize the electronic components used in KID readout for multioctave use and then set up an appropriate readout system to readout arrays of cryogenically cooled KIDs. For this project, the student will learn cryogenics, RF/microwave component characterization, and some detector physics. [In-person opportunity]

687-5 Modelling Thermal Kinetic Inductance Detector Physics Using the G4CMP Toolkit
Paul Szypryt and Daniel Swetz, 303-497-3758, paul.szypryt [at] (paul[dot]szypryt[at]nist[dot]gov)
The thermal kinetic inductance detector (TKID) is a promising new superconducting detector technology that NIST is currently developing for the detection of highly energetic charged particles and gamma-rays. These devices are currently being used for laboratory-scale experiments in quantum information science, and they will also soon be used for fundamental symmetries research at the NIST Center for Neutron Research (NCNR). Although TKIDs have already started to be used in these applications, the underlying physics of these devices is not fully understood. In particular, it is believed that the device performance is limited by the exact arrival location of a photon/particle within the detector. In this project, the student will learn to use the G4CMP simulation tools to model phonon and quasiparticle transport in microelectronic devices.  The student will apply these skills to model the event location dependence in TKIDs and work with NIST scientists to test these models experimentally with cryogenic device measurements. Time-permitting, the student will use G4CMP to explore more complicated TKID phenomena, such as non-uniform side-wall scattering and other sources of energy loss. [In-person opportunity]

Time and Frequency Division (Div 688)

688-1 Software Applications to Support the NIST Time Scale and Time Services
Judah Levine, 303-497-3903, judah.levine [at] (judah[dot]levine[at]boulder[dot]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 ve useful if a student had some experience in elementary statistics, although this is not a strict requirement. [In-person opportunity]

688-2 Measuring Superconducting Circuits with Femtosecond Optical Pulses
Franklyn Quinlan, 303-497-4580, fquinlan [at] (fquinlan[at]boulder[dot]nist[dot]gov)
This project combines state-of-the-art optical, electro-optical, and superconducting circuit technologies to enable new capabilities in cryogenic and quantum information systems. The student will work with a team of researchers to use femtosecond-duration optical pulses to drive superconducting circuits. In this project, the student will learn about ultrashort pulsed laser sources, optical-to-electrical and electrical-to-optical conversion, cryogenic systems, and quantum information systems. [In-person opportunity]

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Created September 28, 2009, Updated January 19, 2024