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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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topics of interest include high-dimensional approximation, closure models, machine learning models, hybrid methods, structure preserving methods, and iterative solvers. Successful applications will work
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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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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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-edge high-performance computing (HPC) that incorporate machine learning/artificial intelligence (ML/AI) techniques into visualizations, enhancing the efficiency and reliability of scientific discovery
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of agentic AI for science, scientific reasoning, federated & collaborative learning, and reinforcement learning (RL) for self-improving models, in the context of leadership scientific workflows and
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electronic structure theory (e.g., density functional theory), and machine learning based computational studies of molecular and periodic systems. The postdoc will also work within a multidisciplinary multi
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), machine learning and Artificial Intelligence to enhance our capabilities in making AI-ready scientific data. As a postdoctoral fellow at ORNL, you will collaborate with a dynamic team of scientists and
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seeking a postdoctoral researcher with expertise in data management, workflow management, High Performance Computing (HPC), machine learning and Artificial Intelligence to enhance our capabilities in making