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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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, 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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to state-of-the-art microfabrication facilities and world-leading supercomputing resources. Interactions with research groups at Fermilab, the University of Chicago, and Northwestern University are highly
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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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photonic platforms through nano- and meso-scale lithographic fabrication. This position supports two complementary, three-year Laboratory Directed Research and Development (LDRD) projects focused on hybrid
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multidisciplinary team of scientists and High Performance Computing (HPC) engineers. In the AL/ML group, we work at the forefront of HPC to push scientific boundaries, carrying out research and development in state
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The Energy Systems and Infrastructure Assessment (ESIA) division provides the rationale for decision makers to improve energy efficiency. We develop and use analytic tools to help the U.S. achieve
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specifically on developing machine learning-based surrogates and emulators for the dynamics of power grids. This role involves creating advanced probabilistic models that capture the complex behaviors
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The Energy Systems and Infrastructure Assessment (ESIA) division at Argonne provides the rationale for decision makers to improve energy efficiency. ESIA develops and uses analytic tools to help