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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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networked systems. It develops community applications, data assets, and technologies and provides assurance to build knowledge and impact in novel, crosscut-science outcomes. The position is supported by
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Confidential Computing and Secure Multi-tenancy. The candidate will be able to make research contributions in areas of system software architectures to support secure computing enclaves on large scale HPC and
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Application-driven Composable Distributed Storage. The candidate will be able to make research contributions in understanding and efficient use of distributed data storage and I/O subsystems for High
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simulations. Design, develop, and validate physics-informed AI/ML models with features from electronic structure, spectroscopy to control materials growth and emerging functionalities. Develop and train agentic
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collaborate with ORNL's AI initiative to advance secure, trustworthy, and efficient AI for science. This position offers a unique opportunity to make significant theoretical and applied contributions
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life cycle energy and cost assessments for conducting manufacturing technology assessments. Excellent oral and written communication skills, including the ability to make technical presentations
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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