223 parallel-computing-numerical-methods Postdoctoral research jobs at Princeton University
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computational approaches, and training the next generation of leaders. PPH seeks applicants for postdoctoral or more senior research positions to join an interdisciplinary group that is tackling a wide variety of
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reasonable estimate for a full-time position; salaries for part-time positions are pro-rated accordingly. The University also offers a comprehensive benefit program to eligible employees. Please see this link
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Postdoctoral Research Associate, Hellenic Studies and the School of Public and International Affairs
offers a comprehensive benefit program to eligible employees. Please see this link for more information. Requisition No: D-26-HLS-00003 PI280077824 Create a Job Match for Similar Jobs About Princeton
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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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The Atmospheric and Oceanic Sciences Program at Princeton University, in association with NOAA's Geophysical Fluid Dynamics Laboratory (GFDL), seeks a postdoctoral or more senior research scientist
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, experience with a variety of programming languages, and familiarity with critical path planning tools, are essential. A Ph.D. in engineering, operations research, computer science, or another related field is
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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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superconductors. The successful candidate must have substantial experience in state-of-the-art ARPES and/or low temperature STM/STS techniques. Some experience with first-principle methods (FP/DFT) and/or other
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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