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for the design and analysis of computational methods that accelerate data analytics and machine learning, especially as the apply to scalable high-performance computing, cloud computing, and large interconnected
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at ORNL. Research activities will include the design of efficient data preprocessing workflows, transforming level-1b large volumes of high-resolution satellite imagery, deployment feature extraction and
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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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management and data intelligence, with a focus on researching and developing advanced data-management technologies, metadata informatics, and scalable solutions to support large-scale environmental 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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expertise in one or more of the following research areas: The design and analysis of computational methods that accelerate AI/ML when applied to large scientific data sets; Energy efficient physics-aware
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computing, clusters, artificial intelligence, quantum, and other advanced computing resources. Compile and debug large science and engineering applications. Identify and resolve system-level bugs in
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for services and equipment, with an emphasis on providing a high level of professionalism and customer services. Examples of subcontracting efforts the candidate will manage are leading competitions for large
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: Ph.D. in Computer Science, Computer Engineering, or a field closely related to the job duties of this position. Demonstrated research in one or more areas of HPC or AI (e.g., large-scale training
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supercomputer and world-class data facilities. This role sits at the intersection of AI at scale and HPC, giving you unmatched resources to prototype new ideas, run large ablations, and translate methods