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materials science. Ability and interest to work in a highly collaborative, interdisciplinary research environment. Preferred Qualifications Experience with graph neural networks, equivariant models
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science and engineering applied to stakeholder-relevant issues. The scientists will collaborate with Princeton and NOAA/GFDL researchers to expand the reach of GFDL's model data (both historical and future
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focus will be on introducing new AI models to chart the chemical âœdark matterâ of the mammalian metabolome; examples of such models include large supervised or self-supervised AI models for mass
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science. The postdoc will work on a variety of projects, which may include methods for large language models, the impact of artificial intelligence on society, as well as broader questions surrounding
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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
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., Datavyu), longitudinal data analysis (e.g., RI-CLPM, growth-curve analysis), multi-level modeling, and experimental study design. Expertise designing studies with parents, infants, and school-aged children