Job opportunities

We have openings for postdoctoral positions in use-inspired AI research, offering opportunities to advance AI that strengthens human–AI teaming and decision-making. We seek researchers motivated to lead an impactful agenda at the frontier of productive, reliable, and grounded decision support. This project is in collaboration with a leading university-based medical center. The postdoctoral researcher will join an interdisciplinary team of faculty and PhD students across UT Austin, benefit from strong mentorship, and work closely with decision-makers through unique access to real-world settings. Successful candidates will also become part of UT Austin’s vibrant AI research community, with opportunities to engage in affiliated AI labs and institutes. Ideal candidates hold a PhD in a relevant field (e.g., Computer Science, Engineering, Data Science or related areas), have a track record and interest in use-inspired AI research, and bring the independence and initiative to start and drive projects while collaborating effectively in an interdisciplinary environment. Required materials to apply: A statement of research, including accomplishments (1-2 pages) 2 representative publications Contact information for 3 individuals who can provide a letter of reference Feel free to reach out to me with questions at maytal@mail.utexas.edu.

The Center for Autonomy is partnering with the Army Educational Outreach Program (AEOP) to provide a new internship opportunity, "Verifiable Integration of Multi-Modal Pre-Trained Generative Models in Sequential Decision-Making for Autonomous Systems". This internship project focuses on advancing autonomous navigation within an existing software stack, while also verifying both new and existing behaviors using large language models. This paid internship provides summer support for two qualified undergraduate students to work with faculty, mentors, and research staff from the Center of Autonomy. See More Details to apply.

The Oden Institute for Computational Engineering and Sciences and the Dell Medical School at The University of Texas at Austin have an opening for a tenured or tenure-track faculty position in Computational Medicine. We are particularly interested in recruiting a colleague whose research program combines mathematical, computational, and physical approaches to solving problems in medicine. Example areas include, but are not limited to, medical imaging, neurology, pediatrics, psychiatry, cardiovascular science, and oncology. See More Details below to apply.

The Oden Institute for Computational Engineering and Sciences and College of Natural Sciences at The University of Texas at Austin invite applications for a tenured or tenure-track faculty position starting in Fall 2026 in the area of Scientific Machine Learning and AI for Science. We seek candidates working on the mathematical and computational foundations of Scientific Machine Learning in a broad sense. Particular areas of interest include, but are not limited to, development and analysis of machine learning models for scientific computing, theory and algorithms for sampling, computational techniques for high-dimensional problems and inverse problems, generative AI applied to scientific problems, and large-scale nonconvex optimization methods used in machine learning. This search is one of several faculty lines committed to expanding the development of AI for Science and Scientific Machine Learning at UT Austin. See More Details below to apply.

The Oden Institute for Computational Engineering and Sciences and the Department of Statistics and Data Sciences at The University of Texas at Austin invite applications for a tenured or tenure-track faculty position starting Fall 2026 in the area of Scientific Machine Learning and AI for science. We are seeking candidates who address challenging scientific and technological problems through advances in statistical and mathematical AI and ML theory and algorithms. Examples of topics of interest include: statistically-principled methods for uncertainty quantification; operator learning and learned surrogates with clear statistical validation; Bayesian inverse problems and data assimilation via measure transport and amortized inference; robustness and distribution shift in scientific ML; causal inference for mechanistic systems; and sequential experimental design and control (including reinforcement learning) under uncertainty. This search is one of several faculty lines committed to expanding the development of AI for Science and Scientific Machine Learning at UT Austin. See More Details below to apply.

The Computational Research in Ice-Ocean Systems (CRIOS) group invites applications for a postdoctoral research position. The successful candidate will contribute to cutting-edge research by developing a high-resolution, coupled ocean and biogeochemistry model of a fjord system in Greenland to conduct interdisciplinary studies to understand connections within the regional system and its response to climate change, and have the opportunity to conduct field work and research visits in Greenland. This NSF-funded project is in close collaboration with the Greenland Institute for Natural Resources (GINR) and Moss Landing Marine Laboratories, San Jose Staté University. See More Details below to apply.