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that accelerate AI/ML when applied to large scientific data sets; Energy efficient physics-aware algorithms, capable of distributed learning on high performance and edge computing; The design of architectures
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Simulation & Data Processing: Use and extension of Allpix2 and TCAD-based simulation tools. Generation of large simulation datasets for algorithm training and validation. Integration of simulation with
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for health research projects. The research activities include HIPAA compliant research data that has been entrusted to ORNL. As such, you will have the opportunity to work on some of the most challenging and
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of government computing, providing the necessary infrastructure to support high-level classified research and development to support data analysis. This position resides in the Project Management Office in
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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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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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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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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