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systems through data-driven modeling and optimization. The successful candidate will work at the intersection of thermal-fluid sciences, control theory, and artificial intelligence/machine learning to
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toolchain to support autonomous “closed-loop” optimization environments, enabling AI agents to analyze, transform, and validate IR with performance-driven reasoning. HPC System Co‑Design : Investigate
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opportunity to work with the world's first exascale system, the Frontier supercomputer, and collaborate with experts in machine learning, optimization, electric grid analytics, and image science. The
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relationships, and distributed optimization of machine learning workflows. Collaborating with world-class scientists, you will enhance your expertise in resource optimization, scalable computing techniques
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candidate will support research and development projects that advance the state of the art in machining science, machine tool design and characterization, manufacturing process optimization, and
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to additive manufacturing (AM), virtual manufacturing, material characterization, topology optimization, and real-time sensing. This position resides in the Computational Sciences and Engineering
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modeling, and process optimization. In this role, you will leverage large-scale, heterogeneous datasets to develop and deploy AI-driven methods for: Real-time quality monitoring and control of
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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
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-solving, conduct experimental campaigns, perform materials synthesis and analysis, sub-component fabrication, process optimization and integration, and prepare technical documents and research
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dynamics (CFD) to support system design and performance optimization. Experience in data center thermal management technologies, including air cooling, liquid cooling, direct-to-chip cooling