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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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to substantially advance this new qubit platform by realizing high-fidelity two-qubit gates. The postdoc will design single electron qubit devices, fabricate them in CNM cleanroom, and characterize them in CNM
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photonic quantum devices for a heterogeneous quantum network, including but not limited to superconducting qubits, microwave-optical quantum transducers, etc. The postdocs will design the devices, fabricate
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engineering, or a related field. Strong programming skills in Python and experience developing research or production-quality machine learning software. Experience with machine learning or deep learning
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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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The overarching focus of the postdoc position and effort is on advancing the understanding of materials at high pressure-temperature conditions, and in particularly across the solid-melt transition
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processes Implement models and perform numerical simulations Analyze and interpret simulation and experimental data Integrate AI tools and workflows to enhance modeling and accelerate learning Collaborate
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The overarching focus of the postdoc position and effort is on advancing the understanding of materials at high pressure-temperature conditions, and in particularly across the solid-melt transition
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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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deep learning including data collection, architecture development, model training, and validation Interest in software development, with particular emphasis on the Python programming language and