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, Margo, MPI, libfabric, etc.) Build CI/CD workflows to validate changes across multiple targets Work with system engineers to deploy DataSpaces on HPC clusters and edge nodes Profile and optimize
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developing parallel/scalable uncertainty visualization algorithms using HPC resources. Collaboration with domain scientists for demonstration and validation of results. Deliver ORNL’s mission by aligning
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techniques for the generation and exploration of complex, large-scale scientific data. Publishing research in leading peer-reviewed journals and conferences. Researching and developing parallel/scalable
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applications. You’ll help design, train, and evaluate AI systems that plan, reason, and take actions to accelerate scientific discovery across domains (materials, chemistry, climate, fusion, biology, and more
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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
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simultaneous recordings and stimulation from multiple, interconnected brain regions. The researcher will gain experience with the use of laminar/neuropixel probes and electrical microstimulation to study
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programming distributed systems; Experience with parallel and distributed File Systems (e.g., Lustre, GPFS, Ceph) development. Advanced experience with high-performance computing and/or large-scale data centers
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, Bioinformatics, Computer Science, Mathematics, Statistics, Data Mining, Parallel Programming, Supercomputing, or Cloud Computing. 3) Experience collaborating with diverse and geographically distant teams