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techniques to solve pressing challenges in energy storage. The successful candidate will work in the Data Science and Learning division of the Computing, Environment, and Life Sciences directorate of Argonne
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and technologies, and in advancing data-driven risk monitoring approaches for supply chain resilience. The candidate will conduct comprehensive supply chain mapping, modeling, and analysis—integrating
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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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time The expected hiring range for this position is $70,758.00-$117,925.00. Please note that the pay range information is a general guideline only. The pay offered to a selected candidate will be
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cryogenic environments Participate in synchrotron-based characterization and data analysis Contribute to high-impact publications, internal reports, and scientific presentations at conferences and workshops
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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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The Applied Materials Division at Argonne National Laboratory has an immediate opening for a postdoctoral appointee. The candidate will perform simulation campaigns to generate data augmenting a
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. Design, implement, and validate experimental setups; conduct synchrotron-based measurements on quantum and energy materials. Build robust data reduction and PDF analysis workflows; document best practices
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will receive full consideration. Key Responsibilities AI-ready data and analysis for the ePIC Barrel Imaging Calorimeter and our Jefferson Lab program Support for the PRad-II and X17 experiments
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experimental electronics and timing systems (e.g., pulse generators, delay generators, gating electronics, and synchronization circuits). • Demonstrated proficiency in x-ray data analysis and instrument control