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

Statistics Interest Group; Pittsburgh (Allegheny County); Presencial; USD 120 000 - 180 000; Tempo integral

Resumo da oferta

Carnegie Mellon University invites applications for a tenure-track Assistant Professor position at the interface of astrophysics, machine learning, statistics, and data science.

The successful candidate will have a primary appointment in the Department of Physics with a joint appointment in either the Machine Learning Department or the Department of Statistics & Data Science, depending on research interests.

Qualificações

• Ph.D. or equivalent in Physics, Astronomy, Astrophysics, Statistics, Computer Science, Machine Learning, Data Science, or a closely related field by the start of the appointment.
• Research at the interface of astrophysics and AI/ML, statistics, and data science.
• Proven ability to teach and mentor students.

Responsabilidades

• Conduct research at the interface of astrophysics and ML, statistics, and data science.
• Teach undergraduate/graduate courses and mentor doctoral and postdoctoral researchers.
• Collaborate with the McWilliams Center and joint departments to advance interdisciplinary projects.

Descrição da oferta de emprego

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Carnegie Mellon University – Tenure-Track Assistant Professor in Astrophysics, Statistics and Machine Learning

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

Company Information

Duties and Responsibilities

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 & 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.

Position 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.

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.

Questions may be directed to search chair Rupert Croft, [e-mail].