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computational science expertise. The Computational Science (CPS) Division focuses on solving the most challenging scientific problems through advanced modeling and simulation on the most capable computers
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This position focuses on the research and development of novel radiation detectors and associated edge-computing circuits and algorithms for X-ray, particle, and nuclear physics experiments
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The High Energy Physics Division at Argonne National Laboratory invites applications for a postdoctoral appointment focused on the design and simulation of advanced detectors for future high-energy
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The Data Science and Learning Division (DSL) of the Computing, Environment and Life Sciences Directorate (CELS) and the Materials Science Division (MSD) of the Physical Sciences and Engineering
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
, large-scale computational science, and simulation of networked physical systems Familiarity with techniques for sensitivity analysis and handling high-dimensional problems Experience in power grid
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, Quantum Information and Quantum Simulation. The successful candidate will be expected to carry out an independent and collaborative research program in particle theory that strengthens and complements
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BatPaC model Understanding of resource recovery technologies, supply chain analysis and upstream and downstream processes Knowledge of Aspen Plus or other chemical process simulation software. Ability
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structural models and compute electronic and vibrational properties. Develop and train neural-network or other machine-learned interatomic potentials to enable large-scale molecular dynamics (MD) simulations
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and simulation of the electronic structure of polycrystalline and defective two-dimensional materials. Interviews will begin immediately and continue until the position is filled. This position centers
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reinforcement learning Experience with high-performance computing, physics-based simulations, and multimodal data workflows Demonstrated ability to train and deploy AI/ML models using simulated and experimental