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Field
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electrocatalysis: The research involves the application and development of ab initio methods, molecular dynamics and kinetic Monte Carlo simulations to model electrocatalytic oxidation and reduction reactions
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. • Familiarity with vibration data analysis techniques. • Experience with Monte Carlo simulation, uncertainty quantification, or sensitivity analysis. • Programming skills in Python, MATLAB, R, or similar
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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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characterization of germanium detectors, develop advanced Monte Carlo models and AI-driven analysis tools to optimize detector response, and to promote precision medical imaging and low energy dark matter searches
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carbon, nitrogen, and water flows in agroecosystems. A solid background in uncertainty quantification, applied statistics, Bayesian calibration, and Monte Carlo simulations. Strong skills in scientific
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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