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The Koel laboratory in the Department of Chemical Engineering at Princeton University is seeking a postdoctoral or more senior researcher position to advance the understanding of liquid metal plasma
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earth system model data, with an emphasis on Seamless System for Prediction and EArth System Research (SPEAR) for seasonal to multidecadal prediction and projection. The project will emphasize elements
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position to advance the understanding of liquid metal plasma-facing materials and first wall concepts for fusion energy devices through surface science experiments. Studies under the controlled conditions of
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, computational fluid dynamics and material science, dynamical systems, numerical analysis, stochastic problems and stochastic analysis, graph theory and applications, mathematical biology, financial mathematics
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earth system model data, with an emphasis on Seamless System for Prediction and EArth System Research (SPEAR) for seasonal to multidecadal prediction and projection. The project will emphasize elements
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position to advance the understanding of liquid metal plasma-facing materials and first wall concepts for fusion energy devices through surface science experiments. Studies under the controlled conditions of
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Department of Chemical and Biological Engineering at Princeton University. The position is in the broad area of renewable energy systems synthesis, analysis, and optimization. The goal of the project is to
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://pritykinlab.princeton.edu) develops computational methods for design and analysis of high-throughput functional genomic assays and perturbations, with a focus on multi-modal single-cell, spatial and genome editing
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, analysis, and optimization. The goal of the project is to develop methods for the synthesis and analysis of systems producing renewable fuels and chemicals; and use these methods, in collaboration with other
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subdivisions of the squamate body plan. The candidate will work towards developing computational resources that assist in the data management and analysis of genomic data and its integration with phenotypic data