161 postdoc-computational-fluid-dynamics-2017 Postdoctoral positions at Princeton University
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, lipid vesicles, polymer physics, active materials, single molecule biophysics, biomaterials, materials chemistry, fluid mechanics, rheology, and computational modeling. Candidates should apply at https
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/acad-positions/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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Values. The teaching will likely involve running a senior thesis seminar rather than teaching a traditional course, subject to approval of Princeton's Office of the Dean of the Faculty. The postdoc will
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Values. The teaching will likely involve running a senior thesis seminar rather than teaching a traditional course, subject to approval of Princeton's Office of the Dean of the Faculty. The postdoc will
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approval of Princeton's Office of the Dean of the Faculty. The postdoc will be expected to participate in a year-long research seminar for visiting fellows and Center faculty, as well as a year-long seminar
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independently while working well in an interactive and dynamic setting. This position is subject to the University's background check policy. The work location for this position is in-person on campus
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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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, lipid vesicles, polymer physics, active materials, single molecule biophysics, biomaterials, materials chemistry, fluid mechanics, rheology, and computational modeling. Candidates should apply at https
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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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interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials