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, analysis, and optimization. The goal of the project is to develop methods for the synthesis and analysis of systems producing renewable fuels and chemicals; and use these methods, in collaboration with other
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performance and continued funding. Postdoctoral Research Associates (PDRAs) are expected to be in residence at Princeton or the local vicinity for the entire academic year or demonstrate to the program's
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Mechanical Engineering, Aerospace Engineering, Chemical Engineering, Chemistry, or a related field is required. The appointment will be for one year with possible renewal based on performance and available
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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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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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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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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