Description

The Oden Institute for Computational Engineering and Sciences and the College of Natural Sciences at The University of Texas at Austin have an opening for a tenured or tenure-track faculty position beginning 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 being conducted jointly by the Oden Institute and the College of Natural Sciences as part of a campus-wide commitment to expanding the development of AI for Science and Scientific Machine Learning at UT Austin.  The successful candidate will be appointed in one of two potential home departments -- the Department of Computer Science or the Department of Mathematics -- based on their area of academic expertise and research interests.   The successful candidate will have half of their teaching duties in the Oden Institute’s Computational Science, Engineering and Mathematics (CSEM) graduate program and half in the Department.  This position is open to applicants at all ranks, with a strong preference for hiring at the assistant professor level.  For more information about the Oden Institute for Computational Engineering and Sciences, please visit https://www.oden.utexas.edu. For more information on the College of Natural Sciences please visit https://cns.utexas.edu/academics/departments.

Requirements

Candidates must have a Ph.D. degree in computer science, mathematics, or a related field. In addition to the ability to teach computer science or mathematics at the undergraduate level, the successful candidate must be qualified to teach graduate classes in the area of computational science, engineering and mathematics. The successful candidate will be expected to create undergraduate and graduate learning environments that address the needs of students from a variety of backgrounds, with differing learning styles and abilities. Further, the successful applicant will be expected to develop an externally sponsored research program, mentor graduate students, collaborate with other faculty, and be involved in service to the university and profession.