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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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Floquet topological phases, Majorana fermions, topological charge fractionalization, topological order and non-abelian anyons. Our group members have backgrounds in topological physics and theory, knowledge
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Science Statistics / Biostatistics Applied Mathematics Data Science Demonstrated expertise in modern machine learning, including at least one of the following: Deep learning (e.g., transformers, sequence models
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inaccessible regimes. Work in the lab combines optics, protein engineering, chemistry, electrophysiology, simulation, and theory. We work at the levels of individual molecules, single cells, and whole
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2 Maximum Number of References Allowed 3 Keywords optics, protein engineering, chemistry, electrophysiology, simulation, theory, physics