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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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. Initiate and maintain open collaboration with researchers across Princeton University. Regularly meet with, listen to, and ask questions of researchers to ensure the engineered solutions fit the research
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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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are responsible for promoting an inclusive and engaging experience for all visitors, modeling behavior for members of the VET, and supporting a variety of mission-driven projects within the Art Museum. The VET
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genome-scale datasets, as well as proved expertise in their curation and analysis using state-of-the-art phylogenetics implementing phylodynamic models. Strong computational skills and programming
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investments. Develop financial models to evaluate alternative tax entity structures. Engage with legal counsel and/or tax advisors on regulatory issues when appropriate. Identify and leverage digital solutions
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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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, modeling behavior for members of the VET, and supporting a variety of mission-driven projects within the Art Museum. The VET currently supports the daily operations of Art@Bainbridge, the Museum’s downtown
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: - Contribute to the development of data models, ETL packages, and business intelligence reports to support organizational initiatives and enable data-driven decision-making. - Actively participate in
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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
to develop hybrid models for sea ice that combine coupled climate models and machine learning. Our previous work has demonstrated that neural networks can skillfully predict sea ice data assimilation