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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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. The ANL ATLAS group maintains strong involvement across the experiment, including detector operations, TDAQ upgrades, Software and Computing, ML development, and High-Performance Computing (HPC
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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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(MSD), and Quantum Information Science (QIS) programs Disseminate results through high-impact publications and presentations at internal and external meetings Position Requirements Position Requirements
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++, or similar languages. Demonstrated expertise in machine learning, especially in the context of dynamical systems modeled by differential-algebraic equations. Experience with high-performance computing and the
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methods that aim to transform scientific discovery and leverage high-performance computing. Specifically, this will include research in : 1. Developing large-scale agent-based and other complex systems
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modeling. Perform predictive modeling using high-performance computing (HPC) infrastructure. Validate computational predictions by collaborating with experimental groups conducting reverse genetics studies
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Laboratory. This is an opportunity to be part of a team helping with the design of new system software and runtime infrastructure to improve the performance and energy efficiency of future scientific computing
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dynamics (CFD) 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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of molecular reactions occurring at the surface of various materials. In addition, computational fluid dynamics (CFD) simulations combined with microkinetic modeling will be carried out to study the heat