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We invite applications for a Postdoctoral Appointee to contribute to a growing research program in process systems modeling and optimization for clean energy, critical materials, and advanced
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phenomena Create new reduced-order models and submodels related to fluid flow, heat transfer, thermochemistry, and electrochemistry in multiphase systems Use modeling tools such as computational fluid
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The Applied Materials Division (AMD) at Argonne National Laboratory is seeking a Postdoctoral Appointee to contribute to the research, development, and manufacturing of advanced cathode materials for next-generation lithium- and sodium- ion batteries. This role provides a unique opportunity to...
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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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The Multi-Physics Computations group at Argonne National Laboratory is seeking to hire a postdoctoral appointee on the topic of CFD modeling of internal combustion engines fueled by low-carbon fuels
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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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modeling tools to develop and optimize new processes and equipment designs using high-performance computing Analyze data, prepare manuscripts for submission to peer-reviewed publications, prepare technical
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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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(2DIR), and 2D electronic-vibrational (2DEV) spectroscopy are desirable but not necessary Familiarity with experimental setup, including computer interfacing and electronics Job Family Postdoctoral Job
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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