66 computer-engineering Postdoctoral positions at Princeton University in United-States
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Engineering Computer Science and Electrical and Computer Engineering Condensed Matter Physics (more...) Materials Physics Quantum Information and Quantum Control Computational Science and Engineering Quantum
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. These highly competitive positions will support research in quantum science and engineering across several departments throughout the university, including physics, electrical engineering, computer
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to computer science, sociology to psychology, engineering to environmental studies - to make novel insights and to tackle the full complexity of human health.We seek applicants to join any of the several
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throughout the university, including physics, electrical engineering, computer science, chemistry, and mechanical engineering. Research areas include foundational quantum science, quantum materials, devices
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: 277839339 Position: Postdoctoral Research Associate Description: The Ferris Research Group in the Mechanical and Aerospace Engineering Department at Princeton University invites applications for a
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for peer reviewed publications Qualifications*Ph.D. in Environmental/Civil Engineering, Computer Science/Engineering, Data Science, or a closely related field*Proficiency in Python or other tools and ML
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the Andlinger Center for Energy and the Environment and the Department of Chemical and Biological Engineering at Princeton University. The position is in the broad area of renewable energy systems synthesis
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The School of Engineering and Applied Sciences at Princeton University seeks applications for a postdoctoral position at the Keller Center for Innovation in Engineering Education. The position is in
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related field (e.g., statistics, computer science, electrical engineering, applied mathematics, or operations research) before May 2025 are encouraged to apply. Ideal candidates will display outstanding
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interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials