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incident angles for benchmarking and validation of theoretical calculations and computational physics and chemistry modeling of important surface processes occurring at plasma-material interfaces in fusion
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patterns and processes that have shaped biological history across deep timescales. The ideal candidate will have a background in phylogenomics and bioinformatics of squamate reptiles; the largest group
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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
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on data science and engineering. The scientist will collaborate with Princeton and GFDL researchers to enhance, analyze and deliver high-resolution earth system model data, with an emphasis on Seamless
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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
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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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, 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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The Princeton University WET LAB (https://ren.princeton.edu/) is seeking a postdoctoral research associate(s) or more senior researcher(s) with expertise and interest in Large Language Models (LLM
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, 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 optimization (theory, modeling, and tools