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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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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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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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the performance and scalability of large-scale molecular dynamics simulations (e.g. LAMMPS) using machine-learned potentials (e.g. MACE) through algorithmic improvements, code parallelization, performance analysis
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, machine learning, and control in the energy sector. The postdoc researcher will perform theoretical study and algorithm development on optimization/control/data analytics methods and authorize peer-reviewed
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++, or JavaScript Experience with AI or machine learning techniques, including large language models or agentic systems Experience developing or integrating interactive visualization systems, including web-based
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applying machine learning or other elements of artificial intelligence to solving significant scientific or engineering problems Interest in software development, with particular emphasis on the Python
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artificial intelligence/machine learning (AI/ML). The successful candidate will contribute to the group’s broad physics program, which includes precision Higgs and Standard Model measurements, and searches
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We are seeking a highly motivated Postdoctoral Appointee with a strong background in artificial intelligence and machine learning (AI/ML), with particular emphasis on the development and application
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to effective therapeutic strategies targeting IDPs Collaborate on the development of open-source machine learning tools to support these therapeutic designs Work closely with high-throughput screening teams