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, distributions, and dynamics in metallic, oxide, and semiconducting systems. This project integrates high-throughput and in situ TEM experimentation with AI/ML-driven image analysis and computational modeling
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of dynamical systems, which will be integrated into large-scale optimization frameworks to enhance the efficiency and reliability of power grid operations. The Postdoctoral Appointee will be responsible
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computational research in accelerator science and technology. The focus is on developing and applying machine learning (ML) methods for accelerator operations and beam-dynamics optimization in advanced
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Argonne National Laboratory is seeking a Postdoctoral Appointee to work in the Advanced Energy Technologies (AET) Directorate. The successful candidate will study methane (CH4) and carbon dioxide
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The Multiphysics Computations Section at Argonne National Laboratory is seeking to hire a postdoctoral appointee for performing high-fidelity scale-resolving computational fluid dynamics (CFD
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into APS beamlines and enabling transformative experiments in X-ray spectroscopy. This is an opportunity to contribute to a growing capability at one of the world’s premier synchrotron facilities
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A postdoctoral appointee position is immediately available at the Advanced Photon Source (APS) at Argonne National Laboratory. Working with scientists from the APS, the successful candidate will
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MSD’s Nanoscale Magnetic and Electronic Heterostructures Group requires expert technical assistance on in-situ electron microscopy characterization of switching dynamics in ferroelectric oxides and
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Investigate how dynamic stimuli manipulate catalyst electronic properties, and how these stimuli can manipulate catalytic elementary steps and reaction outcomes Perform detailed in situ / operando studies
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
modeling of large-scale dynamics in networks. This role involves creating large scale models of dynamic phenomena in electrical power networks and quantifying the risk of rare events to mitigate