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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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some of the following areas: molecular dynamics, Monte Carlo simulations, statistical mechanics, computer programming (e.g., C++, Python), polymer theory, molecular modeling (e.g., of proteins, nucleic
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incident angles for benchmarking and validation of theoretical calculations and computational physics and chemistry modeling of important surface processes occurring at plasma-material interfaces in fusion
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samples, and transcripts. The final candidate will be required to complete a background check. The work location for this position is in-person on campus at Princeton University. Appointments are for one
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researchers working on an NIH funded project focused on developing new systems models to examine social and biological drivers of infection inequality. The overarching goal of this postdoctoral position is to
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, single molecule biophysics, biomaterials, materials chemistry, fluid mechanics, rheology, and computational modeling. Candidates should apply at https://puwebp.princeton.edu/AcadHire/position/38901 and
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required) Kinetic modeling proficiency (Chemkin, Cantera), analytical proficiency (sensitivity, rate of production, etc.) Spectroscopic modeling experience preferred (HITRAN/HITEMP) Familiarity with
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Dr. Bridgett vonHoldt is seeking to hire a postdoctoral associate (or other senior research) in the areas of evolutionary and ecological analyses of large genome datasets, modelling and simulation
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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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background in chemical and biological engineering, bio-engineering, molecular biology, microbiology, biochemistry, biophysics, computational modeling or related fields. Experience in metabolic engineering