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advanced many-body methods, high-performance computing, and machine learning approaches. The successful candidate will play a leading role in developing computational methods and high-performance algorithms
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physics (HEP) detectors, neuromorphic computing, FPGA/ASIC design, and machine learning for edge processing. The successful candidate will work with a multi-institutional and multi-disciplinary team
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research, operations, and community engagement, and work cooperatively to leverage scientific capabilities across ORNL. Work in a highly collaborative environment with data scientists, machine learning
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Analysis and Machine Learning Group. This group focuses on scientific computing with a strong emphasis on scientific machine learning and data analysis. We are specifically interested in applicants with
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to a general science-interested audience as well as a more scientifically trained audience such as researchers and sponsors. Should also have a firm grasp of standard business computer software (e.g
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Requisition Id 15634 Overview: The Data Analytics and Machine Learning (DAML) Group is seeking an exceptional mathematician to work on the development of rigorous theory and algorithms needed
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measurement activities to evaluate the mechanical and thermophysical properties of irradiated materials. Acquire, process, analyze, and report test data in accordance with applicable manuals, procedures, and
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, collaboration, inclusion and continuous learning. Stakeholder Engagement & Partnerships: Serve as the external interface for the center: liaise with sponsors (DOE, other federal agencies, industry, academia
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of interest include structure-preserving finite element methods, advanced solver strategies, multi-fluid systems, surrogate modeling, machine learning, and uncertainty quantification. The position comes with a
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to Computational Fluid Dynamics. Mathematical topics of interest include structure-preserving finite element methods, advanced solver strategies, multi-fluid systems, surrogate modeling, machine learning, and