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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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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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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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/edge processing and device design within the broader project team This position involves close collaboration with researchers at Argonne National Laboratory and Northwestern University as part of the BIA
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skills and familiarity with LLM APIs (e.g., OpenAI API), agent frameworks (e.g. LangChain), PyTorch, and the Python scientific stack (e.g., numpy, pandas, scikit-learn). Experience with front-end
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models for microelectronics materials Curate, manage, and integrate heterogeneous datasets from experiments and simulations Collaborate closely with experimental teams to benchmark and refine computational
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and novel AI hardware to help solve significant real-world problems using machine learning and deep learning. ALCF researchers work in a highly collaborative environment involving science application
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. The successful candidate will work in the Data Science and Learning division of the Computing, Environment, and Life Sciences directorate of Argonne National Laboratories. Primary responsibilities will be
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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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, and flow cells with applications including long duration energy storage and electrified aviation. Each project will involve close collaboration with domain experts to leverage emerging computing