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are especially interested in candidates with strong technical expertise in AI architecture design (e.g., Vision Transformers, foundation models, and federated learning), scalable computing on leadership-class
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platforms and autonomous systems, to characterizing global population risk with increasing spatiotemporal clarity, to designing GeoAI models for supercomputer-scale applications, geospatial science at ORNL is
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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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such as quantization, model pruning, approximate attention (linear and sparse) and proposing new mechanisms for tackling speed, accuracy, as well as energy issues, for large language mode (LLM) inferencing
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experiments, and rapid design/fabrication. NEEM has expertise in neutronics modeling, reactor physics, embedded systems, optical fiber sensing, signal processing and uncertainty quantification. Members
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efficiency and consistency within the metadata team. Utilize good communication skills in English, both written and spoken, to correspond with data submitters and users as needed. Additional activities will be
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in predictions and compressed quantities of interest on defined domains; Fast and scalable algorithms to fit the proposed models to data, with a theory that explains the convergence and success
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United States respond to environmental disturbances. This position resides in the Watershed Systems Modeling (WSM) group in the Environmental Sciences Division (ESD), Oak Ridge National Laboratory (ORNL). ESD is
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security challenges facing the nation. We are seeking a Machine Learning (ML) Research Engineer who will support the development of self-supervised learning methods for large vision-language models
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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