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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
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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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for radiation protection and develops many of the biokinetic and dosimetric models recommended by the International Commission on Radiological Protection (ICRP) and applied by U.S. federal agencies
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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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user community access to expertise and equipment for a broad range of nanoscience research, including nanomaterials synthesis, nanofabrication, imaging/microscopy/characterization, and theory/modeling
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for platform evolution, feature development, and infrastructure modernization. Promote collaborative development models, code review, and shared knowledge repositories. AI-Enabled Instrumentation Design, Control
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modeling, surrogate models, and agent policies to guide experimental decision-making for materials discovery. Develop robust automation pipelines that orchestrate data acquisition, analysis, experimental
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thermomechanics. Major Duties/Responsibilities: Help to develop and apply physics-based and/or machine learning models for advanced manufacturing processes. Author peer reviewed papers for journals and conference
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test facilities. The Section Head leads technical groups, setting technology and operational directions, supporting world-class achievements, ensuring clear project priorities, modeling exemplary safety
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high purity germanium (HPGe) spectrometer systems, which include performing assays in-situ, collecting required parameters, and modeling the unique detector efficiency for each assay. Review and analyze