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, with the goal of demonstrating a path towards fault-tolereant quantum advantage in simulating complex material systems. The position will involve a combination of algorithmic design, numerical
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include, but not limited to, one or more of the following: Design and implement system software to enable AI-readiness for scientific data by developing adaptive techniques capable of maintaining
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) with questions related to this position. Major Duties/Responsibilities: Develop and apply machine learning models (ML) as surrogates for high-resolution process-based hydrologic models. Design and
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physics (HEP) detectors, neuromorphic computing, FPGA/ASIC design, and machine learning for edge processing. The successful candidate will work with a multi-institutional and multi-disciplinary team
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Preferred Qualifications: We are interested in candidates with general research experiences in quantum optics and quantum information science. Priority is given to candidates with experience on the design
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and image data processing. Specific knowledge related to neural network design, training, and optimization is required. You will be joining a group with core expertise in sensor data analytics from
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. Collaborate with researchers outside of our group from diverse backgrounds, within and external to ORNL, including condensed matter theorists and scientists performing device design and fabrication, microscopy
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/Responsibilities: Develop and apply AI foundation models for hydrological and Earth system modeling, with emphasis on improving predictive capabilities for compound flooding in coastal regions. Design and implement
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computing resources. The MMD group is responsible for the design and development of numerical algorithms and analysis necessary for simulating and understanding complex, multi-scale systems. The group is part
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single node between multiple secure workloads. Investigate and evaluate mechanisms for secure encrypted communication across RDMA based networks. Design and evaluate key distribution and management