Tenure-Track/Tenured Faculty Positions in Statistics and Data Science (2026-2027)Apply now DescriptionThe Department of Statistics and Data Science (SDS) at The University of Texas at Austin invites applications for tenured or tenure-track faculty positions, at any rank, to begin in August 2027. We seek exceptional scholars whose work advances our ability to learn from data, whether by directly addressing important
empirical questions in a chosen area of application, or by deepening the foundations of statistical inference and learning. We are particularly interested in four broad directions. First, we seek candidates in applied statistics, scientific machine learning, or AI for science: methodologists embedded deeply enough in another field that they help shape its scientific questions and build new data-analysis methods in
response. The field may be any area of the natural, biomedical, computational, engineering, or social sciences. For such candidates, we regard publication in the leading venues of their chosen field as central evidence of impact, on equal footing with publication in statistics and machine-learning venues. No matter the area, the candidate's record should exhibit methodological advances that statisticians and
machine-learning researchers would recognize as such, even if they first appeared in a domain journal. Second, we welcome research in causal inference, experimental design, and related areas concerned with learning from interventions and designing informative studies. This includes foundational and applied work on how interventions are identified and evaluated, how evidence generalizes across settings, and how
experiments and other data-collection strategies can be designed to answer important questions. Third, we seek scholars working on statistical theory, including the foundations of machine learning and AI. We are interested in fundamental questions about inference, uncertainty, information, learning, robustness, computation, and decision-making. We value theoretical and computational work that provides new
understanding of (or new broad capabilities for) statistics, machine learning, and AI, whether or not it is tied to an immediate application. Fourth, we welcome work that creates broadly useful new computational tools for data-analytic practice, such as statistical computing environments and languages, probabilistic programming systems, scalable inference software, and interactive or AI-assisted tools for data
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