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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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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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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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technical leadership to engineers, architects, and researchers who: Design, develop, and manage scalable data pipelines, systems for large-scale data ingestion, transformation, and delivery, and advanced data
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of computational scientists, computer scientists, experimentalists, materials scientists, and conduct basic and applied research in support of the Laboratory’s mission. Engage with the broader community
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(network flow, log analysis, data visualization, scripting). Expertise with network security monitoring tools (Snort, Suricata, Zeek, Wireshark, tcpdump). Skill in extracting and correlating large data sets
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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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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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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