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A Postdoctoral Appointee opening on the experimental development of optically-active spins for Quantum Information Science at surfaces is available in the Quantum and Energy Materials group
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The Nuclear Science and Engineering (NSE) Division is seeking a postdoctoral appointee to develop computational methods and computer codes to model the physics and engineering of advanced nuclear
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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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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
The Mathematics and Computer Science (MCS) Division at Argonne National Laboratory invites outstanding candidates to apply for a postdoctoral position in the area of uncertainty quantification and
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The Multiphysics Computation Section at Argonne National Laboratory is seeking to hire a postdoctoral appointee for performing multi-physics and multi-scale CFD simulations of aviation gas turbine
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measurements. Postdocs have an initial term of 1 year and can be renewed in 1 year increments; up to a total of 3 years depending on funding and performance. The expected starting date is Q3/Q4 of 2025
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The Multiphysics Computation Section at Argonne National Laboratory is seeking to hire a postdoctoral appointee. The successful candidate’s research will involve synergistic collaborations with a
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, computational physics and x-ray science. The appointee will benefit from access to world-leading experimental and computational resources at Argonne including some of the world’s largest supercomputers (Polaris
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, Physics, or Chemistry. Knowledge of experimental fluid dynamics. Knowledge of mechanical engineering concepts and procedures. Computer and programming skills, including data processing and manipulation
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The Advanced Photon Source (APS) at Argonne National Laboratory invites applications for a postdoctoral position focused on developing novel computational approaches for multi-modal biomedical image