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-aware multi-modal deep learning (DL) methods. At Argonne, we are developing physics-aware DL models for scientific data analysis, autonomous experiments and instrument tuning. By incorporating prior
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Knowledge, Skills, and Experience: Proficiency in mathematical analysis and operator theory. Experience working with microelectronics. Experience in conducting synchrotron experiments and analyzing
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modeling of crystals, dislocation dynamics, and defect analysis, linking atomic-scale simulations to macroscopic properties. Familiarity or interest in machine learning methods and computing frameworks
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equipped with world-leading full-field imaging instruments, including ultrahigh-speed imaging. The group also develops end-to-end scientific software, data analysis, and interpretation methods
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or upcoming year (optional) Experience in one or more of the following areas: experimental data analysis related to hadronic physics, polarized targets or beams, silicon sensors, calorimetry, detector
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research projects, data analysis, physics interpretations, and reporting of the results. A strong background in the field of Experimental Nuclear Physics. Ability to model Argonne’s core values of impact
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, the ALCF is studying the application of these techniques to a variety of our science applications, including but not limited to: Computational Chemistry, Plasma Physics, High Energy Physics, analysis