11 operations-optimization Postdoctoral research jobs at Oak Ridge National Laboratory in United States
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scalability of simulation workflows via: Parallelization and performance engineering GPU/accelerator optimization Algorithmic innovation Experience applying machine learning or AI to molecular simulation
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security. We utilize our expertise in numerical discretization techniques, high performance computing, mesh generation, and geometry representation for a wide variety of physics applications. Our intention
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, inference). Develop agentic AI systems and AI harnessing techniques to enhance model quality, resource optimization, and adaptive execution in diverse workflows. Investigate strategies to balance performance
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‑guided optimizations across languages (Julia/JACC, Mojo/MLIR, Rust/LLVM). Incorporate Enzyme-based automatic differentiation and multi-language IR tooling for AI‑driven analysis. High‑Productivity
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and machine-learning-driven optimization frameworks for polymer composite manufacturing processes. This position resides in the Composites Innovation Group in the Manufacturing Science Division (MSD
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array of capabilities in nuclear nonproliferation, data analytics, cybersecurity, cyber-physical resiliency, geospatial science, and high-performance computing, our organization seeks to produce world
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simulation and flood inundation modeling. River basin planning and operations modeling, including reservoir simulation and optimization. Hydrodynamic modeling of water temperature and quality constituents
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optimization, and application-driven performance analysis for HPC, scientific Artificial Intelligence (AI), and scientific edge computing. We are a leader in computational and computer science, with signature
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Genesis Mission, which seeks to accelerate scientific discovery through the integration of AI-enabled solutions. NCCS operates the Frontier exascale supercomputer and world-class HPC infrastructure, giving
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physics (HEP) detectors, neuromorphic computing, FPGA/ASIC design, and machine learning for edge processing. The successful candidate will work with a multi-institutional and multi-disciplinary team