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, and autonomous materials discovery. This position, supervised by Ashley Bielinski and Alex Martinson focuses on the development of semiconductor materials and the repair of electronic defects
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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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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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We are seeking a highly motivated and flexible postdoctoral researcher to join the Applied Materials Division (AMD) at Argonne National Laboratory to develop advanced methods for in situ and
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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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studies (e.g., EELS, EDS) to probe defect structures and dynamics Apply advanced image processing and analysis; develop AI/ML workflows for quantitative defect characterization Implement high-throughput and
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. The ANL ATLAS group maintains strong involvement across the experiment, including detector operations, TDAQ upgrades, Software and Computing, ML development, and High-Performance Computing (HPC
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symmetries, and nuclear data, and it oversees, operates, and develops advanced detector systems at the ATLAS National User Facility. ATLAS, supported by the Department of Energy’s Office of Nuclear Physics
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contribute to research and model development to enhance the resilience of domestic and global supply chains for clean energy technologies. Lead technical and policy analysis to inform decision-makers
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insights and develop reduced order models (ROMs) for boundary layer flows and turbulent combustion. Integrate ROMs with CFD solvers and demonstrate predictive accuracy compared to traditional modeling