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journals and conferences. This role provides a unique opportunity to work with the world’s first exascale system, Frontier, and collaborate with leading experts in machine learning, optimization, electric
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management machine learning, distributed computing, and resource optimization leveraging the unique computational resources available at ORNL, including the Frontier supercomputer—the world's first exascale
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, industrial energy systems, energy efficiency of manufacturing industry, or other related fields. You will play a crucial role in the planning, execution, and optimization of our technical assistance program
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development Complete simulation verification, model validation, uncertainty quantification, and documentation Optimize system and component designs for performance and safety Author peer reviewed papers
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(ORNL). Major Duties/Responsibilities: Develop and optimize, in collaboration with the manufacturer, a novel Timepix4-based electron detector system, ensuring high-speed data acquisition and enhanced
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learning based manufacturing process development and optimization. This position resides in the Materials Joining Group in the Materials Structures and Processing Section, Materials Science and Technology
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on these topics. Basic Qualifications Ph.D. in Energy/Environmental/Natural Resource Economics or related Applied Economics discipline with a strong quantitative focus. Experience with quantitative optimization
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emerging at the grid edge, providing essential services crucial to its reliable operation. Employing a diverse range of disciplines such as control theory, optimization, economics, game theory, data
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response Demonstrated expertise in process development/optimization for macro-scale deformation in AM Experience with multi-physics simulations on high performance computing (HPC) and maching learning (ML
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to the development and optimization of strategies for proton polarization using DNP techniques for structural analysis. Publish scientific papers resulting from this research and present results at appropriate