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Argonne National Laboratory seeks a Postdoctoral Appointee to perform computational research on materials for thermal and electrochemical interfaces. The successful candidate will integrate first
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at technical conferences. Position Requirements Recent or soon-to-be-completed PhD (typically completed within the last 0-5 years) in mechanical engineering, materials science, civil engineering, computer
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The Data Science Learning Division at Argonne National Laboratory is seeking a postdoctoral researcher to conduct cutting-edge computational and systems biology research. The primary focus
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campaigns using advanced synchrotron X-ray techniques to generate quantitative, AI-ready datasets that reveal defect-mediated mechanisms governing the stability, adhesion, and transport behavior of thin films
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for spin manipulation. This, in the context of spin-defects hosted in platforms such as heterogeneously integrated diamond membranes. A major aim of the work is to explore and implement mechanisms
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of reaction mechanisms in molten salts and apply insights to process development and scale up. Project activities will include the design and development of advanced sensors and flow systems for molten salts
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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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in computational research Comprehensive understanding of quantum mechanics and electronic structure theory is critical Experience with CFD (e.g., the use of OpenFoam or ALDFoam) and microkinetic
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lead efforts to develop experimental techniques using conventional and coherent imaging in the ultrafast time domain, as well as a computational framework for modeling and reconstructing images
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methodologies and tools for economic and ecological analyses of hydropower systems. The position will involve the development and use of computer models, simulations, algorithms, databases, economic models, and