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for computing properties of nuclei. Prepare nuclear data for consumption by machine learning models. Investigate the use of machine learning models to compute properties of nuclei using high-performance computing
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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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for state-of-the-art high performance computing architectures. Study the dynamics and properties of lattice models of nonequilibrium quantum materials using innovative computational techniques. Collaborate
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in the areas of Hydrological and Earth System Modeling and Artificial Intelligence (AI). The successful candidate will have a strong background in computational science, data analysis, and process
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across diverse clients. You will use Frontier's computational power to scale and validate these privacy-preserving algorithms, enabling breakthroughs across energy and image modeling domains. You will also
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of agentic AI for science, scientific reasoning, federated & collaborative learning, and reinforcement learning (RL) for self-improving models, in the context of leadership scientific workflows and
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Earth scientists, geospatial experts, and computational scientists to leverage leadership-class computing resources for large-scale model training, testing, and deployment. Knowledge Dissemination
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Laboratory (ORNL). Major Duties/Responsibilities: Develop models and software for the design and optimization of sensor networks in complex systems, such as water treatment systems Develop physics-based and
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Oak Ridge National Laboratory, Mathematics in Computation Section Position ID: ORNL-POSTDOCTORALRESEARCHASSOCIATE [#27204] Position Title: Position Type: Postdoctoral Position Location: Oak Ridge
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the development of AI architecture for holistic genomic photosynthesis modeling. Evaluate performances of AI genomic photosynthesis models. Report advances to program management and broader scientific communities