146 postdoc-computational-biomedical-engineering Postdoctoral research jobs at Princeton University
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: 275950536 Position: Postdoctoral Research Associate in Microfluidics, Nanofabrication, and Nanophotonics Description: The Department of Electrical and Computer Engineering has opening for postdoctoral
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advanced microscopy techniques and related methods. Candidates who are nearing completion of their Ph.D. (i.e. with a confirmed defense date) or hold a Ph.D. in chemical engineering, chemistry, materials
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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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background in chemical and biological engineering, bio-engineering, molecular biology, microbiology, biochemistry, biophysics, computational modeling or related fields. Experience in metabolic engineering
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: 274769915 Position: Postdoctoral Research Associate Description: The Koel laboratory in the Department of Chemical Engineering at Princeton University is seeking a postdoctoral or more senior researcher
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The Koel laboratory in the Department of Chemical Engineering at Princeton University is seeking a postdoctoral or more senior researcher position to advance the understanding of liquid metal plasma
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both research groups. The Atkinson lab focuses on using protein engineering, electrochemistry, and synthetic biology approaches to control gene expression and metabolism in microbes. The Avalos lab
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: 273379270 Position: Postdoctoral Research Associate Description: The group of Prof. Aditya Sood in the Department of Mechanical and Aerospace Engineering and the Princeton Materials Institute at Princeton
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background in chemical and biological engineering, bio-engineering, molecular biology, microbiology, biochemistry, biophysics, computational modeling or related fields. Experience in metabolic engineering
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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