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3 years) in computer science, materials science, chemistry, physics, mathematics or related engineering disciplines Knowledge of deep learning techniques for time-series and image data Experience with
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Science, Chemistry, Chemical Engineering, Electrical Engineering, Computer Science, Physics, or a related field Demonstrated proficiency in Python and modern ML frameworks (e.g., PyTorch, TensorFlow
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of the upgraded APS, leadership-class computing at the Argonne Leadership Computing Facility (ALCF), and state-of-the-art microscopy at the Center for Nanoscale Materials (CNM). Candidates with a strong background
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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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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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polymer chemistry is required. · Experience with molecular and/or surface characterization techniques (e.g., NMR, Raman, FTIR, electron microscopy, XPS) is required. · Experience with
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scientists with extensive microelectronics (materials and devices), AI, computational materials science and materials characterization expertise; and will be expected to bring the electrochemical expertise
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science, engineering, computational science, a physical science (materials science, chemistry, physics etc.), or related field. Hands-on experience with AI frameworks and employing large language models. Strong Python
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, the ALCF is studying the application of these techniques to a variety of our science applications, including but not limited to: Computational Chemistry, Plasma Physics, High Energy Physics, analysis
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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