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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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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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, benchmarking and deploy them for applications. The ideal candidate would be a PhD in geophysical sciences, computer science, or machine learning with experience in developing and verifying deep learning
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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
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research strongly preferred Experience in deep learning including data collection, architecture development, model training, and validation Interest in software development, with particular emphasis on
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intellectually curious and enthusiastic about computational research. They are intrinsically driven, goal-oriented, and can work collaboratively with others. Working closely with the CPS divison, the postdoc
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. 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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production-quality machine learning software. Experience with machine learning or deep learning frameworks such as PyTorch, TensorFlow, JAX, or similar tools. Knowledge of federated learning
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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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modeling and accelerate learning Collaborate with interdisciplinary experimental and theoretical research teams Prepare manuscripts for submission to peer-reviewed journals Present research findings at