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computational research in accelerator science and technology. The focus is on developing and applying machine learning (ML) methods for accelerator operations and beam-dynamics optimization in advanced
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familiarity in machine learning (ML) and artificial intelligence (AI). This role is pivotal in evaluating the economic competitiveness of the U.S. in the production and manufacturing of energy-related materials
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following component failures Experimentally validating the AI/ML methods on the ATLAS linac at Argonne National Laboratory Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years
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-completed PhD (within the last 0-5 years) in Materials Science, Computational Materials Science, Chemical Engineering or a closely related field. Comprehensive understanding of applied computational materials