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. You will be involved with applied data management activities that support software development for scientific data projects in the Earth, climate, and environmental sciences, such as the Atmospheric
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supercomputer for its dense GPU-based HPC resources to deploy models and create large-scale production datasets for high-impact sponsor missions. The candidate will be expected to handle sponsor requirements and
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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
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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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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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(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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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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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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BlackPearl and large-scale tape libraries to ensure long-term data preservation and accessibility. Integrate high-performance, enterprise, and archival storage layers into cohesive tiered storage architectures