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, fusion, materials, life sciences, and other strategic domains. Investigate novel approaches for balancing efficient I/O, interoperability, and scientific validity in AI-ready datasets. Design, prototype
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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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. Conduct I/O and storage performance characterization of HPC and scientific AI applications or libraries on multi-tier HPC storage systems. Collect, analyze, and leverage telemetry data from HPC systems
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written and oral communication skills. Experience with MPI, OpenMP, parallel I/O (including HDF5), CUDA and/or OpenACC, Fortran, C++, Python. Motivated self-starter with the ability to work independently