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-driven experiment design. Optimize pipelines for performance, parallelization, and near real-time operation during beam time. Contribute to simulation tools to test imaging concepts, predict performance
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information sciences. In parallel with basic research, we develop ideas and technologies further into innovations and services. We are experts in systems science; we develop integrated solutions from care
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effectively with research institutes, industry, colleagues and other stakeholders. The work requires initiative, independence and responsibility. The project interacts closely with other parallel projects and
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University. Our portfolio covers fields from natural sciences to engineering and information sciences. In parallel with basic research, we develop ideas and technologies further into innovations and services
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to cutting-edge research aimed at transforming scientific data management and workflows to enable AI-readiness at scale. You will work on designing system software for automating processes such as intelligent
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scientific drilling vessel Chikyu, including borehole optical fiber strainmeters, borehole pore pressure sensors, and other instruments. In parallel, we are applying state-of-the-art fiber-optic sensing
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workflows to enable AI-readiness at scale. You will work on designing system software for automating processes such as intelligent data ingestion, preservation of data/metadata relationships, and distributed
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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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Science, Computer Science, Applied Mathematics and Statistics, Electrical and Computer Engineering, Biomedical Engineering, or a related field. Experience with a deep learning framework like PyTorch. Strong