10 machine-learning-phd Postdoctoral positions at Oak Ridge National Laboratory in United States
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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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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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materials such as the magnetoelectric, high entropy oxides, through neutron scattering experiments. Additionally, collaborative work will be performed with the aim of developing and applying machine learning
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Division (MSTD), Physical Sciences Directorate (PSD) at Oak Ridge National Laboratory (ORNL). Examples on areas of research interest include but are not limited to: AI/machine learning algorithm development
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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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on the development and application of machine learning algorithms in areas such as surrogate modeling for physical systems, data assimilation, and scientific data reduction. The position comes with a travel allowance
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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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) at Oak Ridge National Laboratory (ORNL). This project will be focusing on the development of advanced Artificial Intelligence (AI)/Machine Learning (ML) tools for the measurements of 3D tensorial strain in
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simulations of reactor core, and other system components Develop reduced-order calibration approaches and apply machine learning and Bayesian calibration methods to enable multi-scale, multi-physics model
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analytics, and machine learning, the Grid Interactive Controls group delves deeply into understanding intricate grid-edge operations. Researchers are dedicated to laying the groundwork for optimal X2G