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tensor network algorithm development and application. Competency with common scientific programming languages such as C++, Python, and/or Julia and version control systems. Experience in parallel
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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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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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research in quantum algorithm development for condensed matter physics. Write peer-reviewed scientific articles and present research findings at major scientific conferences. Collaborate with ORNL staff
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workflows, and distributed intelligence across the computing continuum. In this role, you will have the opportunity to lead and contribute to cutting-edge research aimed at transforming scientific data
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distributed computing runtime with application and compilers to efficiently integrate quantum processors with HPC resources. The research will explore different quantum computing modalities, their
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Engineering at Scale : Profile, model, and optimize distributed codes using MPI, OpenMP, CUDA, ROCm, and related HPC technologies while bridging theoretical AI models with real hardware constraints. Cross
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Engineering at Scale: Profile, model, and optimize distributed codes using MPI, OpenMP, CUDA, ROCm, and related HPC technologies while bridging theoretical AI models with real hardware constraints. Cross
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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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for OPF and related grid simulation tasks. Develop and maintain HPC-ready software workflows for distributed training, large-scale inference, scalable data ingestion, and data management on