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through data-driven modeling and optimization. The successful candidate will work at the intersection of thermal-fluid sciences, control theory, and artificial intelligence/machine learning to advance
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, optimize, and test advanced materials that will accelerate the deployment of higher performance nuclear energy systems. As part of our research team, you will assess the viability of accelerated irradiation
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support the neutronics and thermal-hydraulics analysis. Major Duties/Responsibilities: Design, develop, optimize and analyze reactor models using world-class modeling and simulation codes (SCALE, VERA
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approaches to optimize the trade-off between privacy and utility especially in the context of large models. Advance knowledge of key AI methods such as deep learning, algorithm design, probability theory
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the development and optimization of products for a variety of industries from automotive to aerospace made from new bio- and waste-derived plastic resins and fillers. The ideal candidate for this role would be
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. Optimize system and component designs for performance and safety. Develop agentic workflows for scientific computing. Author peer-reviewed papers, technical reports for internal and external release and
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to additive manufacturing (AM), virtual manufacturing, material characterization, topology optimization, and real-time sensing. This position resides in the Computational Sciences and Engineering Division (CSED
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optimization across quantum and classical computing resources. Conduct hardware-software and application-system co-design by considering interactions among quantum hardware characteristics, HPC architectures
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capabilities in support of dramatic advances in our understanding of the physical world and using that knowledge to address the most pressing national and international concerns. It delivers leadership- class
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modeling, optimal power flow (OPF), surrogate modeling, and data-driven analysis of large-scale electric power system simulations on DOE leadership-class computing resources. The candidate is expected