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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
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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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: - 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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existing efforts in the group and the division. The Argonne High Energy Physics Division provides a vibrant and collaborative research environment. In addition to a strong theory program, the Division has
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define requirements and performance specifications for future HEP/NP detector systems Perform detector concept development, system-level design, and optimization leveraging emerging computing architectures
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-the-loop exploration of extreme-scale scientific data. This position sits at the intersection of scientific visualization, agentic AI systems, human–computer interaction (HCI), and high-performance computing
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The Q-NEXT National Quantum Information Science and Research Center based at Argonne National Laboratory invites applications for a postdoctoral position to conduct research in the field
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for Microelectronics” —a physics-informed AI framework that links composition, structure, and operating conditions to defect evolution and functional performance. The successful candidates will lead experimental
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Argonne National Laboratory seeks a postdoctoral researcher to help build a high-resolution coastal-urban flooding modeling capability within the Energy Exascale Earth System Model (E3SM
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This is an opportunity for a knowledgeable and creative individual to be part of a team using artificial intelligence and high-performance computing to evaluate the state of health (SOH