103 phd-mathematical-modelling-ecological-modelling Postdoctoral positions at Princeton University
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maintaining a shock tube facility (operational proficiency required)Kinetic modeling proficiency (Chemkin, Cantera), analytical proficiency (sensitivity, rate of production, etc.)Spectroscopic modeling
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data dissemination capabilities for making high-resolution earth system model output available to a diverse audience. Candidates must have a PhD in computer science, environmental and physical sciences
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leverage these findings for bioengineering applications. Candidates completing (i.e., with a confirmed defense/viva date) or holding a PhD in chemical engineering, physics, bioengineering, chemistry, or a
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Princeton University, Program in Applied and Computational Mathematics Position ID: 639 -PDRA [#26786, PACM2026] Position Title: Position Type: Postdoctoral Position Location: Princeton, New Jersey
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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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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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, 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
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advance regenerative medicine. For more information about the lab, please visit https://mesa-lab.org/ .Projects will utilize in vivo mouse models, transcriptomic techniques, and advanced intravital imaging
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: 272540354 Position: Postdoctoral Research Associate Description: Ecology and Evolutionary Biology Postdoctoral Research Associate The Department of Ecology and Evolutionary Biology has postdoctoral
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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