57 modelling-and-simulation-of-combustion-postdoc Postdoctoral positions at Princeton University
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discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials. Candidates who are nearing completion
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who are unable to upload unofficial transcripts may send official transcripts to Politics Postdoc Search, Department of Politics, 001 Fisher Hall, Princeton University, Princeton, NJ 08540. A PhD is
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mechanical and aerospace engineering, including but not limited to the fields of: Bioengineering Combustion and Energy Science Computational Science and Engineering Dynamics and Controls Systems Energy and
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the applicant: *Cover letter *Curriculum vitae *Transcripts *Research Proposal indicating plans for two-year postdoc (maximum 5 pages double-spaced) *Dissertation abstract (including Table of Contents) *Writing
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vulnerability modeling, and (c) population and built environment exposure to climate hazards. The broad agenda of this research is assessing the fitness of geospatial indicators to inform conceptual and policy
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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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, to study novel renewable energy technologies. The candidates are expected to have a PhD degree in Chemical Engineering or related field, and have experience with optimization (theory, modeling, and tools
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earth system model data, with an emphasis on Seamless System for Prediction and EArth System Research (SPEAR) for seasonal to multidecadal prediction and projection. The project will emphasize elements
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optimization (theory, modeling, and tools). Candidates should apply at: https://www.princeton.edu/acad-positions/position/39361 and include a cover letter, CV (including a list of publications), research
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, combines advanced system neuroscience and computational modeling techniques to study planning in rodents engaged in dynamic spatial foraging tasks. The successful candidate will develop computational models