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The High Energy Physics Division at Argonne National Laboratory invites applications for a postdoctoral research associate position to conduct research in machine learning (ML) for applications in
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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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The Mathematics and Computer Science Division (MCS) at Argonne National Laboratory is seeking a Postdoctoral Appointee to conduct cutting-edge research in scientific machine learning, focusing
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-aware multi-modal deep learning (DL) methods. At Argonne, we are developing physics-aware DL models for scientific data analysis, autonomous experiments and instrument tuning. By incorporating prior
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to the Lab’s broader effort in CH4 and CO2 utilization R&D. The role will require the individual to work with personnel that perform machine learning and molecular simulations and electrochemical device testing
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fundamental understanding of reaction mechanisms in molten salts and apply insights to process development and scale up. Project activities will include the design and development of online monitoring tools
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novel machine learning models—including Physics-Informed Neural Networks (PINNs), variational autoencoders, and geometric deep learning—to fuse multimodal data from diverse experimental probes like Bragg
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familiarity in machine learning (ML) and artificial intelligence (AI). This role is pivotal in evaluating the economic competitiveness of the U.S. in the production and manufacturing of energy-related materials
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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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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