214 computional-geometry-algorithm Postdoctoral research jobs at Princeton University
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into their models; or 2.Computational social scientists with experience in empirical research and/or theoretical modeling, who are motivated to incorporate their methods into energy modeling. All applicants must
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Center for Globalization and Governance fellowship program, please contact Jennifer Bolton, Assistant Director, at jbolton@princeton.edu. The Princeton School of Public and International Affairs is
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fellow or more senior researcher to work on projects related to computational analysis of chemical and biochemical datasets. A major focus will be on the identification of small molecules from mass
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. The University also offers a comprehensive benefit program to eligible employees. Please see this link for more information. Requisition No: D-26-MOL-00002 PI277839324 Create a Job Match for Similar Jobs About
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of squamate reptiles; the largest group of terrestrial vertebrates on Earth today with 11,000 species. A Ph.D. in Evolutionary Biology, Computational Biology, or related fields, is required. The work will focus
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offers a comprehensive benefit program to eligible employees. Please see this link for more information. Requisition No: D-26-CHV-00001 PI278807957 Create a Job Match for Similar Jobs About Princeton
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this position is in-person on campus at Princeton University. For more information about the Niehaus Center for Globalization and Governance fellowship program, please contact Jennifer Bolton, Assistant Director
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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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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