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learning/artificial intelligence (ML/AI) techniques that incorporate uncertainty into visualizations, enhancing the efficiency and reliability of scientific discovery. It also offers exciting prospects
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development. Basic Qualifications: A PhD in computer science/engineering, electrical engineering, data science or a related field completed within the last five years. Experience of AI and efficient computing
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reinforcement learning and machine vision. Experience with ROS and the ROS ecosystem Special Requirements: Applicants cannot have received their PhD more than five years prior to the date of application and must
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Postdoctoral Research Associate - Energy materials synthesis and exploration with neutron scattering
experiments in the physical, chemical, materials, biological and medical sciences. HFIR also provides unique facilities for isotope production and neutron irradiation. To learn more about Neutron Sciences
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success. Basic Qualifications: PhD in energy engineering, mechanical engineering, electrical engineering, industrial engineering, or a related field completed within the last five years. A strong foundation
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the physical, chemical, materials, biological and medical sciences. HFIR also provides unique facilities for isotope production and neutron irradiation. To learn more about Neutron Sciences at ORNL, please go to
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journals and conferences. This role provides a unique opportunity to work with the world’s first exascale system, Frontier, and collaborate with leading experts in machine learning, optimization, electric
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management machine learning, distributed computing, and resource optimization leveraging the unique computational resources available at ORNL, including the Frontier supercomputer—the world's first exascale
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well as artificial intelligence and machine learning techniques (AI/ML) with emphasis on electronic properties (charge and spin) of a range of materials important to the DOE mission, including the materials classes
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Division, Neutron Sciences Directorate at Oak Ridge National Laboratory (ORNL). You will be part of a dynamic team working on multiple aspects of this problem. You will have a chance to learn about