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activities. This is an exciting opportunity to work within a collaborative group with deep expertise in silicon detectors, Trigger/DAQ (TDAQ) systems, software, and computing. The Argonne ATLAS group plays a
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complex mixtures. Day-to-day responsibilities will include designing and fabricating electrochemical reactor prototypes, conducting electrodeposition experiments in deep eutectic solvent (DES) media
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Experience with developing AI/ML Deep Learning models, working with agentic workflows, model training and other emerging AI techniques and tools Programming experience in Python, C++, or similar scientific
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of Chemical Engineering, Mechanical Engineering, Materials Science, Chemistry, or a related field A deep understanding of electrochemistry, electrochemical engineering, and battery science Strong experience in
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for AI and deep learning (details: NVIDIA DGX-2) Intel-based Aurora Supercomputer: A next-generation supercomputing system (details: Aurora Supercomputer) Additional advanced compute architectures designed
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implement continuous learning approaches that allow models to improve over time as new data, validation results, or experimental feedback become available. Explore agentic AI approaches for federated learning
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deep learning including data collection, architecture development, model training, and validation Interest in software development, with particular emphasis on the Python programming language and
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) Perform low-noise cryogenic measurements of device performance, including timing jitter, dark count rate, detection efficiency, and resonator quality factors Iterate rapidly on fabrication processes and