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performs world-leading research in nuclear structure, nuclear astrophysics, fundamental symmetries, and nuclear data. The Group also manages and operates world-class detector systems as part of the ATLAS
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experimentalists, modelers, and data scientists Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in field of Materials Science, Chemical Engineering, Chemistry, or a related field
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programming, interfacing hardware, and developing machine-learning methods highly desirable. The researcher will join an Argonne funded project with interdisciplinary team of material scientists, computer
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must provide proof of U.S. citizenship, which is required to comply with federal regulations and contract. Skill in devising and performing experiments to acquire identified data, using and maintaining
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or equivalent in the scientific application of this knowledge and practical laboratory experience. Skill in devising and performing experiments to acquire identified data, using and maintaining research equipment
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readout and controls (e.g., SQUID-based time- or microwave-multiplexed systems) with beamline data acquisition and control (EPICS/Bluesky). Develop and maintain data acquisition, calibration, and analysis
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scalability studies to identify and improve bottlenecks in large codes. Experience in development of data-driven reduced-order models in one or more of these areas: turbulence, boundary layer flows, combustion
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(within the last 0-5 years) in field of High Energy Physics Demonstrated excellence in physics analysis, including data analysis, statistical interpretation, and results dissemination Strong general
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performing experiments to acquire data, using and maintaining research equipment, compiling, evaluating, and reporting test results. Problem-solving skills, including the ability to identify technical
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or equivalent. Knowledge and experience with analytical techniques such as XRD and SEM. Skill in devising and performing experiments to acquire data, using and maintaining research equipment, compiling