146 scholarship-phd-agent-based-modelling Postdoctoral positions at Princeton University
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cells (i.e., formation of biomolecular condensates) and to leverage these findings for bioengineering applications. Candidates completing (i.e., with a confirmed defense/viva date) or holding a PhD in
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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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emerging technologies such as artificial intelligence, quantum technologies, and space-based systems, including large satellite constellations. A recent PhD in physics, engineering, computer
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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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their residency at Princeton to assisting with research and to their own work. Eligible candidate must have less than five years of post-PhD research experience prior to anticipated start date. This is a one-year
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University to merge CFPS data with granular COVID mitigation policy data and specify models for evaluation of policy effects. *Communicate with the director of the secure data enclave at Peking
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and contribute to a range of exciting collaborative projects and develop new projects. PhD in a relevant field is required. Strong track record of productivity is essential. Experience with biological
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position. Applicants should have a PhD degree (or expect to receive a PhD degree by June 15, 2025) in Psychology or allied fields (e.g., Sociology) with an interest in conducting research relevant to racial
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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