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modeling, machine learning, and automated experimentation. Mentor and support Group Leaders to ensure excellence in research performance, staff development, inclusion, and cross‑disciplinary collaboration
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at ORNL, along with computational tools for integrated atomistic modeling in support of materials research for extreme environments. The candidates will develop and apply advanced experimental
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power electronics resources modeling, explore different intelligence algorithms to enhance ease of usage of simulations, and different applications of EMT simulations. Selection will be based
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a particular emphasis on error-corrected methods for future fault-tolerant quantum computing. The algorithms will be designed to address key models of quantum materials, such as the Hubbard model
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with lab standards; model proper Environment, Safety, Security, Health, and Quality practices. Major Duties/Responsibilities: Research: Scientists and engineers who have significantly advanced knowledge
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successfully complete and pass a Federal Tier 1 background check investigation. This investigation includes a declaration of illegal drug activities, including use, supply, possession, or manufacture within
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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
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expected to contribute to the development and application of advanced manufacturing simulations, and machine learning (ML) models relevant to additive manufacturing, virtual manufacturing, material
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-impact national security missions. We are seeking a technical contributor in AI/ML research to build models and pipelines, design and implement evaluations and benchmarks, and disseminate results through
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to numerical methods for kinetic equations. Mathematical topics of interest include high-dimensional approximation, closure models, machine learning models, hybrid methods, structure preserving methods, and