Tenure-Track Assistant Professor in Astrophysics, Statistics and Machine Learning

1. a cover letter;
2. a curriculum vitae; and
3. a research statement describing past work, future plans, and how their research has previously and will in future advance the fields of astrophysics and one or more of machine learning, statistics, or data science (maximum length: 4 pages including any figures).

Letters of recommendation are not required at the initial stage.

Applications will be reviewed on a rolling basis beginning 8 November 2026 . Long-listed candidates will subsequently be invited to submit additional materials, including a teaching statement and references.

Applicants should have a Ph.D. or equivalent degree in Physics, Astronomy, Astrophysics, Statistics, Computer Science, Machine Learning, Data Science, or a closely related field by the start of the appointment. Evaluation will consider the strength and potential of the research program, contributions at the interface of astrophysics and the proposed joint field, the ability to work across disciplinary boundaries, and a commitment to excellence in teaching and mentoring.

Carnegie Mellon University invites applications for a tenure-track faculty position at the rank of Assistant Professor at the interface of astrophysics and artificial intelligence, machine learning, statistics, and data science. The successful candidate will have a primary appointment in the Department of Physics and a joint appointment in either the Machine Learning Department or the Department of Statistics and Data Science, according to research interests and expertise.

We seek outstanding researchers whose work addresses fundamental questions in astrophysics or cosmology and engages deeply with machine learning, artificial intelligence, statistics, or data science. Successful candidates will not only use modern computational or statistical methods, but also help advance methodology, understanding, or practice in one of these areas. A particular strength of Carnegie Mellon is its breadth across both modern AI and machine learning and more traditional statistical and data-science approaches. We value research that develops and applies the methods best suited to the scientific problem, drawing on this full range of expertise. We welcome researchers working across observational, computational, or theoretical astrophysics.

The successful applicant will become a member of the McWilliams Center for Cosmology & Astrophysics, a highly interdisciplinary research center with strong connections across Physics, Statistics & Data Science, Machine Learning, and other areas of Carnegie Mellon. The appointment is intended to deepen these connections at a time when major new astronomical surveys, rapidly growing simulation capabilities, and advances in AI are creating new opportunities for discovery.