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The Energy Systems and Infrastructure Assessment (ESIA) division provides the rationale for decision makers to improve energy efficiency. We develop and use analytic tools to help the U.S. achieve
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phenomena Create new reduced-order models and submodels related to fluid flow, heat transfer, thermochemistry, and electrochemistry in reactive systems Use modeling tools such as computational fluid dynamics
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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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The Center for Energy, Environmental, and Economic Systems Assessment (CEEESA) works on innovative research to enhance the resilience, efficiency, and affordability of power grids. Advanced
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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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troubleshooting user issues, delivering training sessions, and creating high-quality documentation to benefit the broader user community. Cross-Team Collaboration: Act as an embedded expert within an ALCF technical
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candidate would be a PhD in geophysical sciences, computer science, or machine learning with experience in developing and verifying deep learning-based models for large dynamical systems (e.g. weather
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models for high-temperature structural materials with applications in nuclear reactors and other energy systems. The candidate will collaborate with ANL staff to review, validate, and enhance methods
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models Disseminate research through publications, presentations, and open-source contribution Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in Materials Science, Data
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The Advanced Photon Source (APS) POLAR beamline is seeking a Postdoctoral Appointee to develop and employ advanced high-pressure instrumentation for x-ray spectroscopy and scattering techniques