This project will consist of an analysis component and, if possible, a hands-on component. The student would follow up on previous work doing a Bayesian parameter estimation to optimize the description of proton-proton collisions at 200 GeV from the Relativistic Heavy Ion Collider by adding additional data and doing a new Bayesian parameter estimation to see how the parameters are shifted. The student may work on implementing additional comparisons to data. If possible, the student may collaborate with the ORNL group on a test beam for the EPIC forward hadronic calorimeter, possibly including travel to the test beam.
Advisor: Professor Christine Nattrass
Enabling quantum behaviors of magnetic systems requires precise control of the crystal's atomic spacing. This is particularly true for lattice geometry where the spins may adopt many distinct configurations at the same energy, giving rise to the so-called magnetic frustration. Changing the atomic spacing can tip balance toward one configuration over another. This project focuses on tuning the lattice parameters by synthesis of a series of (La,Eu)2Ti2O7 crystals with a systematically controlled the chemical composition. The student will test the limits of the La:Eu solid solution to determine the range of the lattice parameter one can possibly reach. The student will also learn how to obtain atomically flat surface of the crystal.
Advisor: Professor Jian Liu
Dark Matter is one of the biggest puzzles of the modern physics. It is known
definitely that Dark Matter exists in the universe. It is even more abundant
than the regular matter. However, the nature of Dark Matter is not understood.
This research project will be focused on the design of a new planned experiment
for detection of Dark Mirror neutrons and on the analysis of experimental data
from a previous experiment.
To be learned: particle oscillations in two-level Quantum Mechanical system,
physics of neutron detectors, McStas neutron transport software, Monte-Carlo
calculation methods, statistics, data analysis and presentations of the results.
Student with computing experience (Python, C++, FORTRAN) are welcome to contact
Prof. Yuri Kamyshkov for an interview.
Advisor: Professor Yuri Kamyshkov
We use a scanning tunneling microscope (STM) to probe various kinds of quantum materials in the atomic scale. By analyzing the atomic and electronic structure acquired by STM, it is possible to reveal the novel quantum states in these materials. In this project, students will be involved in three tasks: (1) use STMs to acquire atomic structure of materials; (2) analyze STM data with advanced statistic tools to extract hidden orders; and (3) automate the STM operation by combining coding for remote operation and machine learning-based methodology.
Advisor: Professor Wonhee Ko
This project focuses on the synthesis of freestanding quantum material thin films using remote epitaxy, an emerging growth technique that enables single-crystalline films to be exfoliated and transferred onto new platforms. By inserting an atomically thin graphene layer between the substrate and the growing film, remote epitaxy enables the formation of freestanding quantum material membranes. These membranes can be integrated into heterostructures of dissimilar quantum materials and provide a versatile platform for strain engineering and the development of novel quantum devices. Students will participate in graphene transfer, molecular beam epitaxy growth of quantum material thin films, exfoliation and transfer of freestanding membranes, and subsequent structural and electronic characterizations. The project provides hands-on training in advanced thin-film synthesis and characterization techniques used in modern quantum materials research.
Advisor: Professor Joon Sue Lee
DNP is the process aligning nuclear spins in a specific direction (typically
that of a magnetic field) in a solid material. In nuclear physics, this is
desirable for a target of scattering experiments as it increases the luminosity
- physics interactions of interest. In biology, DNP is increasingly
utilized to increase the signal to noise ratio in NMR spectroscopy studies, and
is now being used at ORNL to enhance the signal from protein crystal samples,
with the specific goal of utilizing the spin dependent scattering of neutrons
from hydrogen to gain a full atomistic understanding of protein structure.
We will be operating a dynamic nuclear polarization (DNP) characterization
apparatus at UT to benchmark spin polarizable materials, standardize the
preparation of such materials, and develop techniques to maximize the nuclear
polarization. This effort addresses the growing need across several fields
for samples with high nuclear polarization. The samples include
macromolecular crystals for use in neutron beams (biological and materials
science), or deuterated materials to use as targets for electron beams (nuclear
physics).
Advisor: Professor Nadia Fomin
The Nab experiment is starting production data taking, looking at angular correlations in neutron beta decay products. The measured quantities can be used to extract u-d mixing component of the CKM matrix, a vital parameter for the Standard Model of Particle physics. A major effort is under way to develop analysis techniques using realistic simulated decays and apply them to the real data. The student will be part of a team working on this effort and will have the opportunity to interact with researchers from other institutions and national labs and potentially present their work at a conference.
Advisor: Professor Nadia Fomin
The detection of neutrons in radioactive decay and reaction experiments is
one of the big challenges in studies of exotic nuclei. We have constructed a
pixelated detector used for decay studies at the CERN ISOLDE facility and we are
in the process of development a new generation detector for FRIB https://frib.msu.edu/.
We can offer a variety of projects ranging from the development of GEANT 4
simulation to the construction and characterization of prototypes.
Advisor: Professor Robert Grzywacz
In preparation for new experimental campaigns at the Facility for Rare
Isotope Beams (FRIB) and Argonne National Laboratory, we will carry out detector
tests and simulations of the UTK neutron arrays. The work will involve
reassembly of the array after it is returned from CERN-ISOLDE and development of
a new generation of detector response analysis. The student will learn to
operate advanced data-acquisition systems, the basics of neutron detection, and
data analysis and simulation techniques used in experiments with radioactive
beams.
Advisor: Professor Robert Grzywacz
A summer undergraduate research project is available with the CMS experiment at CERN, focused on the Phase-2 upgrade of the Outer Tracker silicon detector for the High-Luminosity LHC. The project emphasizes hands-on participation in the assembly, integration, characterization, and calibration of silicon detector modules, working closely with precision instrumentation and front-end readout electronics. In addition to hardware responsibilities, the project requires strong computing engagement, including familiarity with Linux environments, scripting in Python, version control (e.g., Git), data handling and visualization, and interaction with detector control and configuration software. Experience with C++, basic electronics, or hardware-software interfacing is particularly valuable. The project provides integrated exposure to both hardware development and the software infrastructure required to operate and validate large-scale collider detector systems within an international collaboration.
Advisor: Professor Larry Lee
The CMS experiment at CERN will start colliding protons again this year, opening up new opportunities to search for physics beyond the Standard Model. Students will have an opportunity to help design these searches, studying triggers that make sure our detectors capture the particles we’re most interested in. We also have projects related to the design of future LHC detectors, focusing on an upgrade to the CMS tracker. In addition, there are opportunities in the group to influence where our field may head next by doing studies for a future muon collider. Projects will involve programming, so students are encouraged to build familiarity with python and a unix terminal in advance of the summer.
Advisor: Professor Tova Holmes
The oxygen you breathe, the calcium in your bones and about half of the iron in your blood were formed in the deaths of massive stars as supernovae. We model these explosions in two and three dimensions using supercomputers as the Oak Ridge Leadership Computing Facility and other computing facilities. This summer we plan to study the nucleosynthesis of electron capture supernovae (ECSN), a rare supernova variant. Understanding how many (or even if any) of the supernovae we see each year are actually ECSN requires a detailed understanding of the elements they produce. You will assist in running our nucleosynthesis calculations and analyzing the results so that we may better understand the nuclear contributions from these exploding stars.
Advisor: Professor Raph Hix
We are developing a new toolkit to analyze datasets from core-collapse supernova (CCSN) simulations, including spectral neutrino radiation fields. The toolkit is written in Python. We are in the process of extending it to calculate important diagnostic quantities and visualize datasets from 2D and eventual 3D simulations. Data from an existing, long-term 2D CCSN simulation that was run on Frontier will be used to develop, test, and evaluate new analysis and visualization capabilities. An exploration of the feasibility of using agentic coding frameworks to help with the development of the toolkit could have interest.
Advisors: Professor Bronson Messer and Dr. Vassili Mewes (ORNL)
We are building a new experiment on machine-learning-assisted quantum sensing, and various undergraduate projects are available, including designing and building optical/fiber paths for quantum sensing; generating single photons from a fiber-coupled waveguide crystal; implementing temperature PID control for photon source stability; performing photon detection and time-correlation measurements using a time tagger; supporting electronics/mechanical design and build with Python-based data acquisition/analysis. Prior optics knowledge is helpful but not required - students with interest in experimental physics, optics/photonics, electronics are encouraged to apply.
Advisor: Professor Haocun Yu
A new charged particle detector at the Large Hadron Collider of CERN uses
about 145,000 individual readout channels within an area of 2cm^2. To
analyze the large amount of information from this device, fast neuromorphic
algorithms are developed. The student will participate in setting up the
detector in the SERF laboratory and take measurements with different particle
types. The measurements will be used to tune computer simulations that are
then used to generate samples to train the machine learning algorithms.
Advisor: Professor
Stefan Spanier
We are porting two large astrophysical codebases that we have developed to
the exascale machine Frontier at ORNL. These codebases are written
primarily in C and C++ and implement new, faster and more efficient ways
developed to integrate large sets of coupled differential equations in
astrophysical (and other) environments:
1. the codebase FERN, for application in Type Ia supernova and nova simulations,
and
2. the codebase FENN, for applications to evolution of neutrino populations in
neutrino transport for core-collapse supernova and neutron star merger
simulations.
The largest computers such as Frontier get 90% of their speed from GPU
acceleration and we have demonstrated previously that the new algorithms
implemented in FERN and FENN perform exceptionally well in GPU environments.
Hence, our major current effort is in porting
and optimizing these codebases for Frontier's GPUs. This development, will
enable more realistic simulations of astrophysical events such as
stellar explosions and mergers on leadership class machines like Frontier.
We expect that this will lead to publications with undergraduate students as
coauthors, and presentations by them in seminars and at conferences on this
work. Students are expected to have some programming experience, with past
experience with C and C++ on machines with Linux operating systems and GPU
programming experience desirable.
Advisor: Professor Mike Guidry
The UT-ORNL supernova group is world leading and has produced some of the most sophisticated core collapse supernova models to date. This includes predictions for core collapse supernova neutrino and gravitational wave emission. The predicted neutrino emissions have been processed and analyzed and the results written up in a planned publication. Remaining is the task of predicting what the neutrino detectors such as IceCube, Super-K, Dune, etc. will see, given the emission predictions. This will complete the paper. The student will work with us to process our simulation data with software developed by the neutrino detection community, known as SnoGlobes, for each of the detectors listed above. Given this important contribution to the paper, the student would then be included as a coauthor on the paper.
Advisor: Professor Tony Mezzacappa
Photoelectron Diffraction is a methodology of photoelectron spectroscopy that
allows the determination of the local crystallographic arrangements around an
emitting atom. In one of our research endeavor,
it would be highly important to be able to determine the local crystal structure
around Fe and Se atoms in FeSe layers grown epitaxially on oxide substrates. These are systems that revealed unconventional
superconductivity, with superconducting transition temperatures much higher than
those found in bulk FeSe. The determination of the crystallographic environment
in FeSe monolayers is fundamental for a correct description of their electronic
structure.
We are looking for a highly motivated student who can run simulations with a
dedicated program called EDAC (Electron Diffraction in Atomic Clusters). The
student will have the chance to learn fundamental concepts in solid state
physics, crystallography, and to apply notions of quantum mechanical scattering
to photoelectrons.
Advisor: Professor
Norman Mannella
Angle Resolved Photoemission (ARPES) is one of the premiere techniques that
allow direct measurements of the electronic structure of materials, providing a
link between the macroscopic properties at the heart of the materials
functionality and the microscopic degrees of freedom. With
energy resolutions of a few meV, momentum resolutions of better than 0.5% of a
typical Brillouin zone size, state-of-the-art Angle Resolved Photoemission (ARPES)
experiments are now able to directly reveal the microscopic, many-body
interactions in the single-particle spectral function, making it extremely
powerful in addressing central issues in the physics of complex electron
systems.
Yet, to date, the analysis of ARPES data in multi-band systems has been severely
hampered by the fact that signals derived from different bands overlap,
resulting in broad spectral features that are impossible to resolved. We are looking for a highly motivated student
who can implement machine learning methods for the extraction of bands
dispersion. The student will have the chance to
learn fundamental concepts in solid state physics, crystallography, and to apply
notions of quantum mechanics.
Advisor: Professor
Norman Mannella
We are building a library of the first-ever multi-dimensional many-body neutrino quantum kinetics simulations. These will be toy models, but the physics we learn will inform neutrino behavior in core-collapse supernovae and neutron star mergers. We need a student to help run simulations and analyze data as part of a small group. Some exposure to upper-division quantum mechanics is required. Experience with Julia and creating plots from data and general data analysis (including command line tools) will be helpful.
Advisor: Professor Sherwood Richers
We are building a library of one-dimensional multi-energy mean-field neutrino quantum kinetics simulations. These simulations will be used to train a machine learning model to predict the outcome of the simulations, but we first need to obtain thousands of simulations. We need a student who has experience with command line tools and with Python.
Advisor: Professor Sherwood Richers
In the fall of 2026 we will study the decay of 31F, the most neutron rich isotope of fluorine. No heavier isotopes of fluorine exist, since the nuclear force cannot hold more than 22 neutrons with only 9 protons, the limit known as the neutron drip line. The extreme isotopic asymmet ry leaves clues to the nature of the proton-neutron interaction in the observables we plan to measure, the neutron emission probability and the decay probability distribution. In this project we will characterize the state of the art neutron detector super-3Hen required for this challenging experiment.
Advisor: Professor Miguel Madurga