223 postdoc-parallel-computing Postdoctoral positions at Princeton University in United States
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for independent research as well as opportunities for collaboration with Princeton faculty and graduate students. Postdoctoral Research Associates may participate in the teaching program if mutually agreed, with
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the guidance of Dr. Arash Adel, Assistant Professor in the School of Architecture and Associated Faculty of the Department of Computer Science. The desired start date is Spring 2025. Appointments are for one
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quantitative and computational social science, addressing a diverse array of new data and analytic challenges, facilitating impactful multidisciplinary collaboration, scholarly advancement, and the creation
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specific plans and goals for advancing equity and inclusion if hired as a Princeton postdoc, and contact information for three references. This position is subject to the University's background check policy
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, invites applications for postdoctoral or more senior research position for the 2026-2027 year. Renewal is contingent on satisfactory performance and continued funding. The aim of the program is to promote a
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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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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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at Princeton University.We welcome applications from all areas in mechanical and aerospace engineering, including but not limited to the fields of: Bioengineering Combustion and Energy Science Computational
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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