Tenured or Tenure-Track Faculty Position, Fall 2026-Scientific Machine Learning and AI for Science, UT Austin

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.

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.

Interested applicants should submit the following materials: (1) a cover letter, (2) curriculum vitae, (3) research statement (max 4 pages), and (4) teaching statement (max 2 pages). Additionally, (5) candidates for an assistant or associate professor position must include three reference letters as part of their application packet. Candidates for full professor must provide a list of three reference names but will not submit reference letters.

Candidates should address their submission to the ML/AI Faculty Search Committee. Review of applications will begin November 15, 2025, and will continue until an appropriate candidate is identified. We anticipate virtual interviews in December 2025 and in-person campus interviews in early 2026.

The successful candidate will be required to complete an Employment Eligibility Verification form and provide documents to verify identity and eligibility to work in the USA. A security sensitive background check will be conducted on the applicant selected.

Please address questions to Professors Bjorn Engquist and Keshav Pingali, ML/AI Faculty Search Committee Co-Chairs (engquist@oden.utexas.edu, pingali@oden.utexas.edu). ** For application questions, contact Ruth Park (ruth.park@oden.utexas.edu).

The University of Texas at Austin, as an equal opportunity/affirmative action employer, complies with all applicable federal and state laws regarding nondiscrimination and affirmative action. The University is committed to a policy of equal opportunity for all persons and does not discriminate on the basis of race, color, national origin, age, marital status, sex, sexual orientation, gender identity, gender expression, disability, religion, or veteran status in employment, educational programs and activities, and admissions.