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to numerical methods for kinetic equations. Mathematical topics of interest include high-dimensional approximation, closure models, machine learning models, hybrid methods, structure preserving methods, and
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. The goal of this work is to investigate the dynamics of beams with intense space charge and benchmark simulation models against experimental results. As a U.S. Department of Energy (DOE) Office of Science
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-preserving finite element methods, advanced solver strategies, multi-fluid systems, surrogate modeling, machine learning, and uncertainty quantification. The position comes with a travel allowance and access
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solutions to automate and optimize the interplay between large scientific simulations, data ingestion, and AI processes (e.g., model training, inference). Develop agentic AI systems and AI harnessing
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techniques capable of maintaining relationships between data and metadata. Collaborate on innovative solutions to automate and optimize the interplay between large scientific simulations, data ingestion, and
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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
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, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a travel allowance and access to advanced computing resources. The MMD group is responsible
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to Computational Methods for Data Reduction. Topics include data compression and reconstruction, data movement, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a
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will work with a multidisciplinary team of simulation, design, and technology authorities, and should have a clear passion for R&D and a commitment to fostering team growth. Job Duties and
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and simulation tools, such as spreadsheet-based process cost modeling, input/output modeling, or commercially available life cycle analysis tools such as SimaPro and openLCA. Excellent written and oral