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theoretical research on magnetic and topological properties in van der Waals materials using Density Functional Theory (DFT) calculations, tight-binding and machine learning methods. Provide theoretical
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language models, reinforcement learning, agent learning, robotic control; (4) quantum materials simulation, density functional theory, catalysis and transition-state theory, molecular dynamics; (5
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] - Dynamical Mean-Field Theory (DMFT) and Density Functional Theory (DFT) - Density Matrix Embedding Theory (DMET) - Density Matrix Renormalization Group (DMRG) - Quantum Monte Carlo (QMC) - Tensor network
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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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, Physics, Materials Science, or a related field Three or more years of postdoctoral experience Strong background in first-principles density functional theory calculations, particularly simulations of light
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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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design–test–learn cycles Learn and apply computational methods (e.g. Density Functional Theory) to understand reaction pathways, adsorption and activation mechanisms, and structure–activity relationships