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, or foundation models. Familiarity with atomistic simulations (e.g., density functional theory, molecular dynamics). Interest in developing broadly applicable machine-learning methods for physical sciences
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to validate predictions made by their machine-learning models and drive wet-lab discoveries. The candidate may also have opportunities to work with research software engineers to translate their research
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computing and/or cloud computing; familiarity with Earth system models through model development, model execution, and/or model performance diagnoses; applied mathematics methods such as machine learning
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the application of statistics and machine learning in social science. The position requires no teaching, though teaching opportunities may be provided if requested. When teaching, successful candidates will carry
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spectrometric datasets. A major focus will be on the application of AI/machine learning models and other computational methods to discover unknown metabolites that have strong associations to experimental