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science, including electronic structure methods molecular dynamics, and scientific machine learning. Experience with High-Performance Computing (HPC) systems and intelligent workflows. Demonstrated
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Python and either PyTorch or TensorFlow is required Experience using High-Performance Computers (HPCs) is preferred Ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork
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: - Comprehensive understanding of applied computational materials science, including electronic structure methods and molecular dynamics. - Experience with High-Performance Computing (HPC) systems and intelligent
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interfaces. Programming and HPC: Strong scripting and data analysis skills; experience with high-performance computing environments and job schedulers. Demonstrated ability to work in multidisciplinary teams
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machine learning models at a world-class high-performance computing facility The candidate will have access to state-of-the-art computing resources, including: NVIDIA DGX-2 Systems: Powerful platforms
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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 data management and high-performance computing, including workflow design and optimization Strong oral and written communication skills, with the ability to work effectively with internal and
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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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-Informed Neural Networks (PINNs) and geometric deep learning. Experience with active learning, agentic workflows, or other methods for autonomous experimentation. Familiarity with high-performance computing
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reinforcement learning Experience with high-performance computing, physics-based simulations, and multimodal data workflows Demonstrated ability to train and deploy AI/ML models using simulated and experimental