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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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, 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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each semester, assist in organizing research-based events (e.g., speaker series, symposia, or reading groups), present their work at research methods and field-specific seminars, assist with DDSS
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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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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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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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employment. Individuals with expertise in relevant modeling methods are encouraged to apply, including but not limited to integrated assessment modeling, energy systems modeling, supply chain modeling, etc
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, or reading groups), present their work at research methods and field-specific seminars, assist with DDSS projects on occasion, and offer consultation to faculty, graduate students, and postdoctoral researchers