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

CMU group; Pittsburgh (Allegheny County); On-site; USD 110,000 - 150,000; Full time

Job summary

Carnegie Mellon University invites applications for a tenure-track Assistant Professor in the Physics Department with a joint appointment in the Machine Learning Department or the Statistics and Data Science Department, depending on research interests.

We seek scholars addressing fundamental questions in astrophysics or cosmology and leveraging ML, AI, statistics, or data science; applicants should demonstrate potential to teach and mentor across disciplines.

Qualifications

• Ph.D. or equivalent degree required by start of appointment.
• Research at the interface of astrophysics and AI/ML/Statistics/Data Science.
• Ability to work across disciplinary boundaries and teach/mentor across fields.

Responsibilities

• Develop and lead a vigorous research program at the intersection of astrophysics and ML/DS.
• Teach courses and supervise undergraduate, graduate, and postdoctoral researchers.

Skills

Teaching & mentoring

Education

Ph.D. or equivalent in Physics, Astronomy, Astrophysics, Statistics, Computer Science, Machine Learning, Data Science or related field

Job description

Carnegie Mellon University: Mellon College of Science: Physics

Location

Open Date

Oct 14, 2026

Description

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.

Qualifications

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 is an equal opportunity employer. It does not discriminate in admission, employment, or administration of its programs or activities on the basis of race, color, national origin, sex, disability, age, sexual orientation, gender identity, pregnancy or related condition, family status, marital status, parental status, religion, ancestry, veteran status, or genetic information. Furthermore, Carnegie Mellon University does not discriminate and is required not to discriminate in violation of federal, state, or local laws or executive orders.