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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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modern data science/machine learning techniques to a wide variety of subjects including nuclear material detection, identification, characterization, and localization capabilities to support nuclear
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machine learning and high performance computing to help develop high-fidelity computational tools that are used for large-scale, physics-based simulations of fusion energy systems in partnership with
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Generative AI and Machine Learning skills. This position resides in the AI Operations Program office within the Application Development Division of the Information Technology Services Directorate. Our AI/ML
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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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Generative AI and Machine Learning skills. This position resides in the AI Operations Program office within the Application Development Division of the Information Technology Services Directorate. Our AI/ML
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changing needs. Strong, proven publication record. Self-motivated. Preferred Qualifications: At least 2 years of relevant work experience. Proficiency with C++, Python, AI, machine learning. Experience
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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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Postdoc Research Associate - Quantum Materials Synthesis and Characterization, AI-Enabled Automation
reflectometry. Focus on new materials design linking experimental data with machine learning strategies. Assist staff in harnessing emerging AI and LLM technologies for automated synthesis, characterization, and
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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