Providing education, research, problem-solving, and service in nuclear science and engineering
RadLab
The RadLab at The University of Texas at Austin focuses on research using radiation and radioactivity to improve security and quality of life.
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Reactor
The NETL reactor, designed by General Atomics, is a TRIGA Mark II nuclear research reactor. The NETL is the newest of the current fleet of U.S. university reactors.
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Robotics
The Nuclear and Applied Robotics Group is an interdisciplinary research group whose mission is to develop and deploy advanced robotics in hazardous environments in order to minimize risk for the human operator.
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Home
$20M+
In funding for molten salt reactor development
60+
Graduate students
$1.7M
Research expenditures per tenured/tenure-track faculty in FY23
News
New Publication on the economic viability of nuclear power in Texas
Current PhD student Ivy Seidel published the peer-reviewed paper, Investigating nuclear energy viability in Texas with decision making model GenX, in the journal Energy Economics. This paper focuses on the analysis of the economic viability of Nuclear Power in Texas. Seidel utilized open source capacity expansion model GenX to weigh the cost of unserved energy against the cost of implementing nuclear power in the face of electricity demand growth Texas. ERCOT is expected to experience a dramatic increase in the number of data centers being constructed due to the favorable economic landscape of the state, this would greatly impact industrial power draw and need to be accounted for in the coming years. A sharp rise in power demand along with expected population growth is predicted to cause a near doubling of the average electricity demand in Texas by 2030.
Register for Fireside Chat with Isabelle Boemeke March 24th
Join the UT Nuclear Niche for an in-person fireside chat with Isabelle Boemeke on Advocacy in the Last Nuclear Renaissance. Use the QR code below or this link to register.
đź“Ť Rowling 5.210
đź—“ Tuesday, March 24, 2026
⏰ 5:30–7:00 PM
New Publication by Dr. Seo
Dr. Jeongwon Seo's paper, Softmax-Based Deep Neural Network in Regression, was recently published in the Journal of VVUQ. While traditional AI regression models are great at giving a single "average" answer, they often struggle with the messy, unpredictable nature of real-world data.
To overcome this, Dr. Seo's approach borrows a clever tactic from classification: by breaking continuous outputs into discrete 'bins', the model can predict not just a single number, but the entire landscape of probabilities.
UT Austin moderates TNA panel on building a nuclear workforce in Texas
Dani Zigon, Director of Strategic Initiatives for The University of Texas at Austin’s Nuclear & Radiation Engineering Program, recently served as moderator and panelist for the Texas Nuclear Alliance (TNA) webinar, “The Path Forward for Cultivating Homegrown Nuclear Talent in Texas.” A full recording can be found on YouTube.
PhD candidate wins MARC XIII Poster Competition
Bradley Gladden, a current PhD candidate, was recently selected as the winner of the MARC XIII Student Poster Presentation Competition for her work titled "Modeling Fluorinated Spent Nuclear Fuel in a Molten Salt Reactor."
