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University of Rochester.

Assistant Professor (math, biology, physics, brain and cognitive science)

University of Rochester., Rochester, New York, United States

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The School of Arts & Sciences (SAS) at the University of Rochester is excited to announce a new cluster of faculty hires committed to bridging mathematics with the natural and physical sciences. We seek researchers who use mathematical and computational models to further insightful, predictive, and quantitatively scalable explanations of the world. This cluster–generously supported by the Simons Foundation–will consist of

four tenure-track Assistant Professor positions

distributed across applied mathematics (to be housed in the Department of

Mathematics ), complex systems and quantum information science ( Physics & Astronomy ), evolutionary genetics & genomics ( Biology ), and neural information processing underpinning human cognition ( Brain & Cognitive Sciences ). The successful candidate is expected to establish an externally funded research program and contribute to teaching and research mentoring at the undergraduate and graduate levels. SAS is a multidisciplinary college that affords opportunities for intellectual interactions across diverse disciplines. The University of Rochester offers strong institutional support, including state-of-the-art computing infrastructure, and a rich environment for interactions beyond the hiring departments, including with the Goergen Institute for Data Science and Artificial Intelligence, the Hajim School of Engineering and Applied Sciences, and the University of Rochester Medical Center. Requirements and Application Details: Ph.D. in math, biology, physics/astronomy, or neuroscience or brain and cognitive science. For full consideration, applications should have all materials submitted by November 1, 2025. The anticipated start date of the position is July 2026. Salary range: $90,000 - $120,000 The referenced pay range represents the full base range of pay for this job. Individual salaries will be determined within the job’s salary range and established based on market data, experience, and expertise of the individual, and internal equity considerations.

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