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 Department of Statistics and Data Science 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 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. These topics are meant as illustrations, and not as an exhaustive list. This search is being conducted jointly by the Oden Institute and the Department of Statistics and Data Science 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 have half of their teaching duties in the Oden Institute's Computational Science, Engineering and Mathematics (CSEM) graduate program and half in the Department of Statistics and Data Science. This position is open to applicants at all ranks, with a preference for hiring at the assistant or associate 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 Department of Statistics and Data Science please visit https://stat.utexas.edu.

Candidates must have a Ph.D. degree in computational science, statistics, data science, computer science, mathematics, engineering, or a related field. In addition to the ability to teach statistics or data science, the successful candidate must be qualified to teach graduate courses in the CSEM program. 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 SciML/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 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 Prof. Omar Ghattas and Prof. Alessandro Rinaldo, SciML/AI Faculty Search Committee Co-Chairs (omar@oden.utexas.edu, alessandro.rinaldo@austin.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.