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at temperatures of 50 mK-350 K and at fields of up to 15 T with emphasis on the development of new sensing protocols optimized for high-field and low temperature environments. In addition, these sensors will be
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. Proficiency in bioinformatic pipeline development and optimization. Experience analyzing and interpreting multi-omics datasets. Strong background in statistical analysis and data integration. Experience with
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and optimize molten salt thermophysical property measurements, develop and utilize theoretical models and frameworks to predict salt properties, molten salt thermophysical property database expansion
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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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engineering or equivalent field and 0-2 years of research experience. Background in quantitative analysis, mathematical modeling, data science and simulation techniques with expertise in optimization techniques
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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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, 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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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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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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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