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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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structure methods, quantum Monte Carlo, tensor networks, or quantum embedding methods, etc. - ML-augmented numerical method development. - High-performance computing (HPC
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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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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 14 days ago
Monte Carlo Collisions (MCC) methods for the simulation of ionized gases is the primary qualification, as these particlebased tools represent a significant component of the broader kinetic–continuum
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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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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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-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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studies Monte Carlo simulation and/or statistical software or package development (e.g., R, Stata) Multilevel modeling (MLM), difference-in-differences (DID), or comparative interrupted time series (CITS
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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