29 coding-"https:"-"Prof"-"FEMTO-ST" "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "St" positions at Argonne
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The Energy Systems and Infrastructure Assessment (ESIA) division provides the rationale for decision makers to improve energy efficiency. We develop and use analytic tools to help the U.S. achieve
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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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related field. Experience with finite element simulations and developing constitutive models. Knowledge of high temperature creep crack growth. Knowledge of engineering design codes such as the ASME Boiler
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engine modeling code. Perform high-fidelity CFD simulations of turbulent and reacting flows pertaining to gas turbines and detonation engines using spectral element method (SEM). Perform scalability
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advanced detector systems. ATLAS is supported by the Department of Energy (DOE) Office of Nuclear Physics and hosts 300-500 users annually from universities and national laboratories in the U.S. and
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functions of this position successful applicants must provide proof of U.S. citizenship, which is required to comply with federal regulations and contract. Illegal drug testing as defined in 10 CFR 707.4 and
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microelectronics project. To learn more: Argonne to lead two microelectronics research projects under U.S. Department of Energy initiative | Argonne National Laboratory Position Requirements Recent or soon-to-be
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., specific code you wrote, modules you debugged, or workflows you designed). Highlight Transferable Skills: If your background is in a specific science domain (e.g., Physics, Biology), frame your experience in
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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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have a strong background in fundamental electrochemistry, with preferable hands-on expertise in computational materials science. The applicant should be well versed in code development, application of AI