96 phd-mathematical-modelling-ecological-modelling Postdoctoral positions at Princeton University
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The Andlinger Center for Energy and the Environment at Princeton University seeks applications for two interdisciplinary postdoctoral research or more senior research positions to analyze and model
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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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. A major focus will be on the identification of small molecules from mass spectrometry-based metabolomics data, in part based on generative AI models of chemical structures. The position is available
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researchers at Princeton and other institutions, to study novel renewable energy technologies. The candidates are expected to have a PhD degree in Chemical Engineering or related field, and have experience with
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skillsExpertise in Generative AI: Strong background in machine learning, with specific experience in Large Language Models (LLMs), and Vision-Language Models (VLMs)Excellent programming skills (Python is required
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on the identification of small molecules from mass spectrometry-based metabolomics data, in part based on generative AI models of chemical structures. The position is available starting July 2025, and will remain open
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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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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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/neuropixel probes and electrical microstimulation to study attention and decision making networks in a behaving animal model together with parallel studies in humans. The project is part of a NIMH Silvio O
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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