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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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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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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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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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large-scale training and post-training pipelines (including distributed data/compute and evaluation harnesses). Collaborate with domain scientists and external partners; co-develop end-to-end AI workflows
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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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-based modeling of hydrological and Earth system processes. The CHAS group conducts world-class research in hydrological and Earth system modeling, large-scale data analytics and machine learning (ML), and
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safety at ORNL and DOE sites. This position resides in the Performance Engineering group in the Data and AI Systems Section in Computer Science and Mathematics division within Computing and Computational
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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