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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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unique opportunity to engage in transformational research that advances the development of AI-ready scientific data, optimized workflows, and distributed intelligence across the computing continuum. In
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computing software libraries (e.g., Trilinos, MFEM, PETSc, MOOSE). Experience with shared and distributed memory parallel programming models such as OpenMP and MPI. Experience with one more GPU or performance
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for transmission or distribution grids, synchronous generators, large loads, transmission networks, etc. Develop simulation algorithms that enable large-scale simulations. Integrate (or co-simulate) grid component
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/O solutions (e.g., HDF5, ADIOS2), and distributed computing tools relevant to data preparation. Evidence of ability to conduct independent research and publish in peer-reviewed venues. Preferred
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. Demonstrated experience developing and running computational tools for high-performance computing environment, including distributed parallelism for GPUs. Demonstrated experience in common scientific programming
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and physical processes for recovering and purifying radioisotopes from irradiated targets and waste streams. These efforts directly support the DOE IP mission to produce and distribute limited-supply
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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
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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 minimize