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on creating innovative artificial intelligence algorithms for the trusted visualization of large-scale 3D scientific data. This position resides in the Data Visualization Group in the Data and AI Systems
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reactions, as well as nuclear data. The position is part of the nuclear physics team that resides in the Advanced Computing for Nuclear, Particle, and Astrophysics group at the National Center
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solutions for large-scale scientific data models in federated learning environments. You will advance privacy-preserving machine learning by developing efficient techniques that maintain robust privacy
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Department of Energy (DOE). ORNL’s CCP conducts world-class research and development in multi-scale computational coupled physics, large scale data analytics and DL, and model-data integration at the DOE’s
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Associate who will focus on creating innovative uncertainty quantification and visualization algorithms that enable trusted visual representation and analysis of large-scale 2D/3D scientific data
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-driven 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
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, including applied mathematics and computer science, experimental computing systems, scalable algorithms and systems, artificial intelligence and machine learning, data management, workflow systems, analysis
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solutions to automate and optimize the interplay between large scientific simulations, data ingestion, and AI processes (e.g., model training, inference). Develop agentic AI systems and AI harnessing
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, artificial intelligence and machine learning, data management, workflow systems, analysis and visualization technologies, programming systems and environments, and system science and engineering. Major Duties
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techniques capable of maintaining relationships between data and metadata. Collaborate on innovative solutions to automate and optimize the interplay between large scientific simulations, data ingestion, and