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, computer science, or engineering within the past 5 years. Previous theoretical and/or computational research experience in tensor networks, Monte Carlo, machine learning or a related field Proficiency in quantum
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quantum magnetism and strongly correlated systems, as well as classical methods such as exact diagonalization, tensor networks or DMRG, and quantum Monte Carlo. Familiarity with inelastic neutron scattering
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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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-changing needs Preferred Qualifications: Experience in radiological risk assessment Experience in biokinetic model development Experience with Monte Carlo radiation transport software and applications
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, reinforcement learning, monte-carlo tree-search, causal ML etc. Design, develop, and validate interpretable cross-modal AI/ML models incorporating features from electronic structure theory for predictive
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in multiscale and multifidelity simulation techniques (ab initio methods at different fidelity, machine learning tight-binding, machine learning force fields, phase-field modeling, and/or kinetic monte