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and transient inverter modeling and different applications of the simulation. Selection will be based on qualifications, relevant experience, skills, and education. You should be highly self-motivated
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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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for Risk Forecasting, Large Language Models (LLMs), and Human-in-the-Loop AI Systems. Our aim is to advance AI for Operations by integrating next-generation AI agents and LLMs with real-world operational
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AI processes (e.g., model training, inference). Develop agentic AI systems and AI harnessing techniques to enhance model quality, resource optimization, and adaptive execution in diverse workflows
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breaking in nature, the limits of nuclear stability, and signatures of new physics beyond the Standard Model. Major Duties/Responsibilities: Develop formalism and methods for computing properties of nuclei
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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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computing AI on High-Performance Computing (HPC) cluster. Examples on areas of research interest include but are not limited to: Vision transformers. AI foundation models. Computing and energy-efficient
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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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. 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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research analysis on geothermal well development and other advanced energy technologies that could achieve transformative gains in energy efficiency. Ability to develop optimization and life cycle models