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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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of spintronics. Complemented with density functional theory (DFT) calculations to build scientific and technical competence as well as strengthen transferrable skills, this position provides you with the skills
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simulations, density functional theory (DFT), molecular simulations, or machine-learning potentials. Experience with generative AI, active learning, uncertainty quantification, Bayesian optimization
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functional theory studies of nanoparticles, surfaces, or bulk materials; Chemical reaction modeling and/or enhanced sampling (NEB, metadynamics, microkinetic modeling, etc.); Molecular dynamics simulations
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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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complex magnets via interpretable machine-learning models, and develop improved AI models that can accelerate prediction of new synthesizable magnet candidates with high energy density and critical