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sector energy use modeling, optimization, and analysis, encompassing both supply-side and demand-side technology transformations needed for achieving near zero emissions by 2050, a goal also often referred
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that integrate simulation, machine learning, and data analysis. Numerical optimization methods (e.g. machine learning including deep neural networks, reinforcement learning, data mining, genetic algorithms
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resulting from the deployment of new technologies. If you are passionate about sustainable energy solutions and optimizing supply chains for a more energy-secure future, we invite you to apply
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, including object stores, memory-X and filesystems, to research and develop strategies to evaluate, profile and optimize AI applications at scale. Another key objective is to help us with design of future
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(LLMs) to enhance visualization and analysis. Responsibilities include conducting research, optimizing visualization algorithms, applying AI-driven approaches, collaborating across disciplines
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HEP-CCE (Center for Computational Excellence) Storage Optimization. The HEP Division performs cutting-edge research facilitated through advanced detector development, high-performance supercomputing
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, optimize, test and run large-scale parallel applications on HPC systems. Job Family Postdoctoral Job Profile Postdoctoral Appointee Worker Type Long-Term (Fixed Term) Time Type Full time The expected hiring
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environment where all can thrive. Preferred Qualifications: Familiarity with high performance computing (HPC) software platforms. Ability to develop, optimize, test and run large-scale parallel applications
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also lead integrated process modeling, develop models for predicting technology learning rates and commercialization timelines, and contribute to technology design and optimization. Additionally
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develop strategies for satisfying applicable requirements. Investigate performance of nuclear fission reactors and contribute to optimization of reactor design and operational strategies. Perform analyses