60 cloud-computing-"https:"-"https:"-"https:"-"https:" positions at Argonne in United States
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. This position offers an exciting opportunity to contribute to fundamental and applied research in materials chemistry using advanced computational techniques and artificial intelligence. The project involves: 1
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Extensive knowledge of Microsoft Excel and good computer programming skills Knowledge of techno-economic analysis and life cycle analysis Experience working with Argonne’s EverBatt model, GREET model, and
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Intelligence, Machine Learning, Quantum Information and Quantum Simulation. The successful candidate will be expected to lead an independent research program in particle theory to strengthen and complement
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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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limited to, ATLAS at CERN, the South Pole Telescope, and the Simons Observatory. The candidate is also expected to work closely with computational experts at the Computational Science (CPS) division
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symmetries, and nuclear data. LER also plays a critical role for the ATLAS National User Facility, where it provides support for ATLAS Users, conducts its own research program, and develops and operates
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and program managers. Position Requirements Minimum Education / Experience Requirements: A Ph.D. in physics, applied physics, electrical engineering, or related field. Additional Requirements: Normal
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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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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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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