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Field
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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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that advances the development of AI-ready scientific data, optimized workflows, and distributed intelligence across the computing continuum. In this role, you will have the opportunity to lead and contribute
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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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for the design and development of numerical algorithms and analysis necessary for simulating and understanding complex, multi-scale systems. The group is part of the Mathematics in Computation (MiC) Section
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to advanced computing resources. The MMD group is responsible for the design and development of numerical algorithms and analysis necessary for simulating and understanding complex, multi-scale systems
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development of numerical algorithms and analysis necessary for simulating and understanding complex, multi-scale systems. The group is part of the Mathematics in Computation (MiC) Section
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 16 minutes ago
. Description: Uncertainty quantification is a field with wide applicability to the design, development, and science return of space science missions. There is growing interest for the tools and practices
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, lead large-scale benchmarking across the full stack, and develop scalable classical simulations (e.g., tensor networks)—including performance bounds beyond brute-force classical simulability. This role
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. Experience with phase retrieval algorithms, clean room use and e-beam lithography are beneficial. The candidate will be expected to participate at international user facilities and thus will be expected
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the full stack, and develop scalable classical simulations (e.g., tensor networks)--including performance bounds beyond brute-force classical simulability. This role is deeply collaborative with the Advanced