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Department of Chemical and Biological Engineering at Princeton University. The position is in the broad area of renewable energy systems synthesis, analysis, and optimization. The goal of the project is to
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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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, Research Systems and Critical Infrastructure, the position will provide IT support to ensure the optimal operation and reliability of critical systems, devices and applications used in laboratory animal
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, optimization, and future transformation of our Human Resources Information Systems. This role will play a pivotal part in ensuring the accuracy, integrity, and usability of employee data across the organization
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the optimal operation and reliability of critical systems, devices and applications used in laboratory animal research. This role will collaborate with researchers, animal care staff, and IT professionals
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, immunology and/or cancer biology is highly desirable along with the ability to perform advanced mouse work. -Optimally, the candidate has demonstrated experience with molecular biology and laboratory rodent
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that software systems, critical infrastructure, desktop support, and data analytics systems are optimized for operational efficiency, security, and regulatory compliance. This position entails direct, hands
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the ability to perform advanced mouse work. -Optimally, the candidate has demonstrated experience with molecular biology and laboratory rodent handling and tissue culture. -Computational biology experience is
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of research that PPPL supports are the following topics: Machine Learning for Digital Twins, Foundation Models, and surrogates (surrogate model building from our suite of fusion codes, new optimization
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following topics: Machine Learning for Digital Twins, Foundation Models, and surrogates (surrogate model building from our suite of fusion codes, new optimization techniques, and differentiable programming