149 postdoc-density-functional-theory-dft Postdoctoral positions at Princeton University
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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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, 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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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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differential equations, computational fluid dynamics, material science, dynamical systems, numerical analysis, stochastic analysis, graph theory and applications, mathematical biology, financial mathematics
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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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, anticipates offering a number of postdoctoral or more senior research positions in theory, observation and instrumentation, including (but not limited to): the Lyman Spitzer, Jr. Postdoctoral Fellowship
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some of the following areas: molecular dynamics, Monte Carlo simulations, statistical mechanics, computer programming (e.g., C++, Python), polymer theory, molecular modeling (e.g., of proteins, nucleic
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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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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
to develop hybrid models for sea ice that combine coupled climate models and machine learning. Our previous work has demonstrated that neural networks can skillfully predict sea ice data assimilation