Tenure-Track/Tenured Faculty Positions in Stati...

American Statistical Association - ASA; Austin (TX); On-site; USD 110,000 - 180,000; Full time

Job summary

The Department of Statistics and Data Science (SDS) at The University of Texas at Austin invites applications for tenured or tenure-track faculty positions to begin in August 2027. We seek scholars who advance learning from data, addressing empirical questions or deepening foundations of statistics and learning.

We welcome candidates across four directions: applied statistics/ML/AI for science; causal inference and experimental design; statistical theory; and broadly useful computational tools

Qualifications

• Doctoral degree in statistics, biostatistics, computer science, machine learning, applied mathematics, or closely related discipline by August 2027.

Responsibilities

• Develop an impactful research program and contribute to teaching, mentoring, and service.
• Maintain and grow an externally funded research program with scholarly output.

Education

PhD in statistics / related field

Job description

Description

The Department of Statistics and Data Science (SDS) at The University of Texas at Austin invites applications for tenured or tenure-track faculty positions 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.

Description

The Department of Statistics and Data Science (SDS) at The University of Texas at Austin invites applications for tenured or tenure-track faculty positions 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 analysis. For such candidates, widespread adoption of their tools by researchers and practitioners is evidence of impact on equal footing with publication.

SDS is one of three founding departments of UT's new School of Computing, alongside Computer Science and Information. We welcome candidates whose work creates opportunities for collaboration with colleagues in those departments or elsewhere at The University of Texas at Austin, including through joint appointments.

Our department is internationally recognized for its research in statistical methodology and theory, machine learning, applied statistics, Bayesian inference, and biostatistics, and its faculty are committed to excellent teaching in statistics and data science for students from across the School, the University, and in SDS's own degree programs. The department currently has 26 tenured/tenure-track faculty, including 9 joint faculty with primary appointments in other departments. UT Austin is one of the most intellectually vibrant universities in the country, with abundant opportunities for interdisciplinary research within the College of Natural Sciences and School of Computing, and across the Dell Medical School, the Oden Institute for Computational Engineering and Sciences, the Population Research Center, the Machine Learning Laboratory, the Center for Generative AI, and many other research centers across the campus. A partnership with the Texas Advanced Computing Center (TACC) provides access to world-class computing resources. The department and university are committed to supporting the professional development of all members of the faculty. The teaching load for tenured/tenure-track faculty in SDS is two courses per year.

Austin, the capital of Texas, is a center for high-technology industry, including companies such as 3M, Amazon, AMD, Apple, Applied Materials, AT&T, Dell, Google, IBM, National Instruments, and Samsung. Much of Austin’s lifestyle is driven by outdoor activities, media, and music.

More information about the department is here: https://stat.utexas.edu/

Qualifications

Candidates should have a doctoral degree in statistics, biostatistics, computer science, machine learning, applied mathematics, or a closely related discipline by August 2027.

Candidates for a tenure-track Assistant Professor position are evaluated based on their potential for developing an impactful research program and for becoming excellent in teaching, mentoring, and service.

Candidates for a tenured Associate or Full Professor position are expected to exhibit a strong independent program of externally funded research along with established records of excellence in teaching, mentoring, and service.