82 computational-physics "https:" "https:" "https:" "https:" "Ulster University" Postdoctoral positions at Argonne
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Knowledge of atmospheric dynamics, process scale models, and numerical computation techniques Knowledge of data analysis Knowledge of using atmospheric observational datasets, data assimilation techniques
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Argonne National Laboratory invites applications for a postdoctoral research position in experimental physics, with a focus on advancing superconducting particle detector technology for next
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advanced computing, optimization, and data analytics technologies. The postdoctoral researcher will work with a team of researchers on solving challenging problems using optimization, stochastic models
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on understanding novel and emergent behavior in nanoscale magnetic heterostructures, particularly in confined 2D van der Waals magnets and related devices. The goal of the program is to study and control magnetic
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for Microelectronics” —a physics-informed AI framework that links composition, structure, and operating conditions to defect evolution and functional performance. The successful candidates will lead experimental
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information science and light–matter engineering, while engaging with CNM’s cleanroom and characterization capabilities, APS ultrafast and nanoprobe X-ray beamlines, MSD’s THz initiatives, and Q-NEXT’s national quantum
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3 years) in computer science, materials science, chemistry, physics, mathematics or related engineering disciplines Knowledge of deep learning techniques for time-series and image data Experience with
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Overview The Argonne Wakefield Accelerator (AWA) Group in the High Energy Physics Division at Argonne National Laboratory seeks a postdoctoral research associate to conduct experimental and
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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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of radiofrequency (MHz–GHz) nanoscale phenomena in systems relevant to microelectronics and quantum information science. Opportunities also exist for cross-platform studies integrating ultrafast TEM with ultrafast x