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the operation of a wide range of surface science methods, with emphasis on HRXPS, LEIS, AES, LEED, TPD, HREELS, Raman scattering, and SEM. Qualified candidates should possess experience for the design
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meetings. Additional Information: Applicants must hold a PhD in demography, sociology, epidemiology, or a related field. The work location for this position is in-person on campus at Princeton University
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senior ranks may have multi-year appointments. Individuals should have or be expected to have a PhD with appropriate research experience in computational biology, chemistry, biochemistry, computer science
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to conduct numerical studies for non-local flow-driven magnetized plasma instabilities and their associated nonlinear transport in astrophysical and laboratory plasmas. The applicant is required to have a PhD
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models, programming, and quantitative methods. Preferred qualifications include experience in reinforcement learning, neural networks, and/or statistics. Questions can be addressed to Professor Nathaniel
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performance and continued funding; those hired at more senior ranks may have multi-year appointments. Candidates must have a PhD in Computer Science or a related field. For full consideration, we recommend
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. Qualifications: Successful candidate will have a Ph.D. in Philosophy, Religion, or a related field and must have less than three years of post-PhD research experience prior to anticipated start date. Applicants
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emerging technologies such as artificial intelligence, quantum technologies, and space-based systems, including large satellite constellations. A recent PhD in physics, engineering, computer
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availability of funding. The anticipated start date for the position is June 1, 2025. Individuals with a strong theoretical background who expect to obtain a PhD in a related field (e.g., statistics
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. Some experience with first-principle methods (FP/DFT) and/or other forms of electronic and magnetic structure theory and calculations is also expected. The successful candidate will have a strong