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key element of the two-beam acceleration concept Emphasize Bayesian optimization approaches and integrate these methods into the facility control system Design, execute, and analyze accelerator
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The High Energy Physics (HEP) Division at Argonne National Laboratory invites applications for a Postdoctoral Research Associate to join the ANL ATLAS group with a focus on physics analysis and
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, distributions, and dynamics in metallic, oxide, and semiconducting systems. This project integrates high-throughput and in situ TEM experimentation with AI/ML-driven image analysis and computational modeling
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of molten salt chemistry and electrochemistry Develop novel process monitoring and control technologies applicable to molten salt and liquid metal systems Develop advanced molten salt flow systems to enable
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Demonstrated expertise in electrocatalysis, including catalyst synthesis, electrochemical characterization, and mechanistic analysis Hands-on experience with in situ/operando spectroscopy and/or advanced
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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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candidate will conduct research related to a wide range of topic areas including the analysis of energy and power systems, optimization of veriable energy resources, electricity market design and operation
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computational scientists to advance a next-generation, user-friendly, agentic AI platform for automated data analysis, interpretation, and user interactions. The appointment is expected to last two years and the
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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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for electro-optic modeling Desirable Skills Data analysis using Python Experience with autonomous or AI-assisted synthesis workflows Familiarity with quantum transduction or quantum information science