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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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that integrate with existing or new large language models and large vision models for resource optimization with energy grid data. Provide coding support to implement privacy preserving federated learning
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version control, CI/CD, testing frameworks, configuration management, and scalable computing architectures. Familiarity with high-performance computing (HPC), data management workflows, or large-scale data
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service offerings (e.g., large-scale geospatial compute pipelines, data ingest/curation/archive, analytics/visualization, user support). Establish operating policies, SLAs, user workflows, resource
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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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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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vision transformer or large vision AI model Expertise in high performance computing Expertise in image and spatiotemporal data processing Expertise in federated learning on large computing clusters A
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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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in high-performance computing and data analytics with applications in a large variety of science domains and NCCS is home to some of the fastest supercomputers and storage systems in the world
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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