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to apply. We seek candidates with expertise in some or all the following areas: density functional theory, deep learning, high-throughput simulations, molecular dynamics, and materials chemistry. Strong
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Engineering, Chemistry, or a related field is required. The appointment will be for one year with possible renewal based on performance and available funding. This is a full-time position; work must be
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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
learning. Our previous work has demonstrated that neural networks can skillfully predict sea ice data assimilation increments, which represent structural model errors (https://doi.org/10.1029/2023MS003757
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://puwebp.princeton.edu/AcadHire/position/36402 and submit a cover letter, CV, a research statement that includes your specific plans and goals for advancing equity and inclusion if hired as a Princeton postdoc, and
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, and protein engineering. The group is part of the Department of Chemical and Biological Engineering (https://cbe.princeton.edu/). Qualified candidates will be highly motivated, with a strong
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positions to work in experimental condensed matter physics with focus on angle-resolved photoemission (ARPES) and scanning tunneling spectroscopy (STS/STM) based studies of topological, strongly correlated
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samples, and transcripts. The final candidate will be required to complete a background check. The work location for this position is in-person on campus at Princeton University. Appointments are for one
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://puwebp.princeton.edu/AcadHire/position/36402 and submit a cover letter, CV, a research statement that includes your specific plans and goals for advancing equity and inclusion if hired as a Princeton postdoc, and
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simulations, statistical mechanics, computer programming (e.g., C++, Python), polymer theory, molecular modeling (e.g., of proteins, nucleic acids, ligands), coarse-grain and polymer model development
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differential equations, computational fluid dynamics, material science, dynamical systems, numerical analysis, stochastic analysis, graph theory and applications, mathematical biology, financial mathematics