20 algorithm-development-"Multiple"-"Simons-Foundation"-"Prof"-"UNIS" "U.S" Postdoctoral positions in United States
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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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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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Requisition Id 15448 Overview: We are seeking a Postdoctoral Research Associate who will focus on creating innovative artificial intelligence algorithms for the trusted visualization of large
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 2 hours ago
supporting multiple probes simultaneously. Swarms also provide the usual benefits of multi-element array reception, namely robustness to single point failures and transmit/receive diversity. The downside
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of application development techniques (numerical methods, solution algorithms, programming models, and software) at scale (large processor/node counts). A record of productive and creative research as proven by
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for transmission or distribution grids, synchronous generators, large loads, transmission networks, etc. Develop simulation algorithms that enable large-scale simulations. Integrate (or co-simulate) grid component
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large quantities of data to gain a greater understanding of our systems and develop data analytics and artificial intelligence algorithms. You will be actively engaged in the research and development
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training algorithms and AI architecture. Image reconstruction, segmentation, and classification. High performance computing for spatiotemporal data. Major Duties/Responsibilities: Develop foundation AI
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develop signal processing algorithms to characterize structural health in microreactors and other advanced nuclear reactor technologies. Metrics for success will include scientific output, disseminating
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postdoctoral scholars, working on the development of a core, scalable methodology. This methodology leverages existing spatial data on landscapes, fire behavior, and fuel treatments to evaluate real-world