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experiment research program, particularly in the ATLAS experiment. The successful candidate is expected to take a leading role in data analysis, detector construction, and experiment operations. In
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The Chemical Sciences and Engineering Division is seeking a highly qualified and motivated postdoctoral researcher to join our team in the area of light-matter interactions, with a particular focus
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The Computational Science Division (CPS) at Argonne National Laboratory (near Chicago, USA) is seeking a postdoctoral researcher to enable exascale atomistic simulations of ferroelectric devices
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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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Quantum Theme, focusing on Next-Generation Quantum Systems. The successful candidate will lead efforts to discover and design quantum emitters with desirable properties for quantum information science (QIS
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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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Argonne's Legal Department. All Argonne offers of employment are contingent upon a background check that includes an assessment of criminal conviction history conducted on an individualized and case-by-case
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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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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