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with environment, safety, health and quality program requirements. Maintain strong dedication to the implementation and perpetuation of values and ethics. Deliver ORNL’s mission by aligning behaviors
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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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learning algorithms in PyTorch. Expertise in object-oriented programming, and scripting languages. Parallel algorithm and software development using the message-passing interface (MPI), particularly as
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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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, 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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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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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