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at the intersection of applied mathematics, computational science, and high-performance computing. The successful candidate will help develop advanced scientific computing methods and deploy them on state-of-the-art
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of Quantum Monte Carlo (QMCPACK, PYQMC) density functional theory (e.g. QE, VASP, PYSCF) and associated models to describe various properties of DOE-relevant quantum materials. The Materials Theory Group has a
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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
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