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Application-driven Composable Distributed Storage. The candidate will be able to make research contributions in understanding and efficient use of distributed data storage and I/O subsystems for High
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Requisition Id 16798 Overview: The Data and AI Systems Research Section/Workflow systems Group within the Computer Science and Mathematics Division at Oak Ridge National Laboratory (ORNL) is
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requirements, and opportunities for quantum advantage. Research and develop quantum runtime technologies for heterogeneous QHPC distributed systems, including workload mapping, scheduling, resource
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programming in C++, MPI, OpenMP, CUDA, or related models. Solid understanding of distributed-memory systems and many‑core architectures. Preferred Qualifications: Formal Methods : Experience with automated
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Postdoctoral Research Associate to develop, scale, and apply artificial intelligence (AI) and deep learning (DL) models for power grid systems. The successful candidate will contribute to scalable AI
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areas include: Parallel and distributed graph and or ML algorithms — shortest paths, connectivity, clustering, or graph traversal on GPU clusters and distributed-memory systems. Sparse direct and
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scientific data to advance research efforts across scientific systems. Develop and apply federated learning on distributed and heterogenous datasets. Develop more efficient and resilient DP techniques that
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deformation of metal diaphragms for various harsh environment applications. Develop data acquisition and control systems that include hydraulic or pneumatic actuation and pressure monitoring combined with
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Requisition Id 16723 Overview: We are seeking a Postdoctoral Research Associate with expertise in artificial intelligence (AI) and machine learning (ML) for multiscale physical systems. This
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You will contribute to the development of integrated data and AI workflows that span data acquisition, modeling, and decision-making, including deployment at the edge and across distributed systems