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, classification, and interpolation of pixel detector data. Exploration of spike-based data encoding/decoding strategies. Hyperparameter optimization using advanced computing resources (e.g., HPC clusters). Detector
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engineers, leveraging cutting-edge resources; most notably the Frontier supercomputer, the world's first exascale computing system. This is a unique opportunity to engage in transformational research
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AI-ready scientific data. As a postdoctoral fellow at ORNL, you will collaborate with a dynamic team of scientists and engineers, leveraging cutting-edge resources; most notably the Frontier
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supercomputing resources. The candidate will also contribute to the development of large-scale identity and key management solutions. We are a leader in computational and computer science, with signature strengths
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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
. Create and maintain datasets in databases on in-house data storage resources working closely with ORNL’s workflow and data management scientists. Meaningfully collaborate with experimental groups involved
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. Compose technical reports, create presentations, and publish peer-reviewed papers Support the development of new resources, training, and tools to support companies participating in the DOE’s Better Plants
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other MEERA Group industrial technical deployment (Better Plants) and Energy System Software Tools development projects. Help support the development of new resources, trainings, and tools to support
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interconnection topologies that simplify the use of diverse distributed storage resources through advanced methods for distributed data placement, layout, tiering, and movement. Major Duties and Responsibilities
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Research Associate to develop and apply computational technique for advanced manufacturing using high-performance computing resources. ORNL’s CCP conduct world-leading research and development in multi-scale
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leading peer-reviewed journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources. Collaboration with domain scientists for demonstration