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engineering, or a closely related field, in hand by the date of appointment. ● Research experience performing first-principles electronic structure or density functional theory calculations using
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materials, organic solids, molecular materials, or other solid-state systems. Experience with computational approaches relevant to structural characterization, including density functional theory (DFT
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Science and Engineering, Physics, Chemistry, or a closely related discipline. You have a strong expertise in atomistic and molecular simulation techniques (density functional theory, molecular dynamics, ab
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at the atomic scale, using density functional theory-accurate machine-learned potentials and molecular dynamics simulations, in close collaboration with leading European research institutes and steel industry
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position within a Research Infrastructure? No Offer Description Activities and context: The fellow will develop machine-learning interatomic potentials (MLPs), trained on density functional theory-DFT data
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of Prof. Georg Madsen, with regular shorter research stays at Aarhus University. The project combines density functional theory (DFT), machine-learned force fields and atomistic simulations to uncover how
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will participate in conducting multiscale modeling such as quantum mechanics density functional theory calculations, molecular dynamics simulations, Monte Carlo simulations, and/or machine learning
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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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, 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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well as with theoretical researchers specializing in atomistic simulation, density functional theory (DFT), and ab initio molecular dynamics (AIMD). The successful candidate will also engage with collaborators