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expertise in synthesizing, fabricating, and characterizing materials (conductive metal-organic frameworks, 2D & hierarchical structures, single-atom catalysts, etc.) for electrochemical energy conversion and
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modern AI/ML methods to protein and cellular biology, including protein structure prediction, protein–protein and small-molecule–protein docking, cell state prediction from large-scale Perturb-seq datasets
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systems at various scales, for example using ab initio electronic structure methods like density-functional theory, developing interatomic potentials with various methodologies including machine learning
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funded. The successful applicant will work in Professor Carlos Argüelles’ group on the development, construction, and deployment of TAMBO Phase 1. Candidates with strong expertise in data acquisition