60 computational-intelligence Postdoctoral positions at Oak Ridge National Laboratory
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liquids, frustrated magnetism, excitonic magnets, and strongly correlated electron systems. You will work closely with theorists, experimentalists, and computer scientists to build robust, scalable
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publications and presentations. The Learning Systems Group at the Oak Ridge National Laboratory focuses on artificial intelligence and computational research and applies this knowledge to support the nation’s
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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
that can incorporate multi-scale computational simulations to aid with data fusion across multiple modalities of experiments with the final goal of discovering novel materials phenomena or even new materials
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Requisition Id 15217 Overview: Oak Ridge National Laboratory (ORNL), the U.S. Department of Energy’s largest multi-program science and energy laboratory, has an extraordinary 80-year history
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challenges facing the nation. The Computational Coupled Physics (CCP) Group within the Computational Sciences and Engineering Division (CSED), at Oak Ridge National Laboratory (ORNL) is seeking a Postdoctoral
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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
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Requisition Id 15603 Overview: The National Center for Computational Sciences (NCCS) at the Oak Ridge National Laboratory (ORNL) is seeking a postdoctoral research associate in the area of HPC
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potential for high-impact research contributions at the forefront of computational quantum many-body physics. This position resides within the Computational Chemistry and Nanomaterials Sciences group in
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that advances the development of AI-ready scientific data, optimized workflows, and distributed intelligence across the computing continuum. In this role, you will have the opportunity to lead and contribute
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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