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Computing (HPC) system architecture and intelligent storage design. The candidate will contribute to research and development efforts in scalable storage and memory architectures, telemetry-driven system
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characterization of HPC and scientific AI applications or libraries on multi-tier HPC storage systems. Design and evaluation of approaches for time-sensitive or data-intensive processing of data originating
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experiments, classical simulations, and quantum simulations. Collaborate with ORNL staff scientists and external collaborators. Write peer-reviewed scientific articles and present research findings at major
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committed team of scientists to develop composite pipe technologies for geothermal energy or similar harsh environment applications. Design, develop, fabricate, and test composite material in lab scale and
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innovation and play a key role in driving forward significant advances in fiber technologies. Major Duties/Responsibilities: Developing cutting-edge polymer chemistries for advanced fiber applications
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avoidance, autonomous exploration, frontier selection, and localization-aware trajectory planning. Develop high accuracy point cloud registration and mapping workflows. Plan and conduct experiments
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will design and implement differential privacy solutions for large-scale scientific data models in federated learning environments. You will advance privacy-preserving machine learning by developing
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workflows to enable AI-readiness at scale. You will work on designing system software for automating processes such as intelligent data ingestion, preservation of data/metadata relationships, and distributed
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engineering, or a related discipline completed within the past five years. Research experience in machining science, machine tool dynamics, machine tool error compensation and modeling, CNC system design, and
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single node between multiple secure workloads. Investigate and evaluate mechanisms for secure encrypted communication across RDMA based networks. Design and evaluate key distribution and management