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experiment research program, particularly in the ATLAS experiment. The successful candidate is expected to take a leading role in data analysis, detector construction, and experiment operations. In
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chemistry, chemical engineering, physics, computational science, materials science, or related field. Background in synchrotron characterization techniques. Experience collecting and analyzing large
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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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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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The successful candidate will be highly motivated and have a strong track record in problem solving and scientific publications. The candidate will be expected to conceive of, plan, and implement scientific
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-throughput workflows for data acquisition and analysis Contribute to on-the-fly data processing and integration with computational tools Collaborate with multidisciplinary teams in nanofabrication
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lead efforts to develop experimental techniques using conventional and coherent imaging in the ultrafast time domain, as well as a computational framework for modeling and reconstructing images
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electrocatalyst materials Plan and execute in situ/operando studies using advanced techniques such as X-ray Absorption Spectroscopy (XAS), X-ray Photoelectron Spectroscopy (XPS), Raman spectroscopy, Differential
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to develop and lead a world-class research program that strongly aligns with DOE priorities in low energy nuclear physics, as outlined in the 2023 Nuclear Science Advisory Committee Long Range Plan for Nuclear
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focus on further advancing the ATTA technique. The Physics Division has an active and broad-ranging program at the intersection of nuclear and atomic physics including a strong focus on fundamental