330 distributed-computing-associate-professor positions at Oak Ridge National Laboratory
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maintenance of the ORNL water distribution system (including potable and fire water), the wastewater collection and treatment system plant, and the storm drain system. This position is the resident authority
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issues Preferred Qualifications: Experience supporting research computing, High Performance Computing, or large-scale data platforms Knowledge of distributed and high-performance file systems Experience
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Informatics portfolio. You will help develop modular and scalable software tools and infrastructure for data processing and data center operations in the Earth, climate, and environmental sciences, such as the
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Informatics portfolio. You will be expected to work with a team as well as independently research and develop AI-driven methods to enhance discovery and usability of Atmospheric Radiation Measurement (ARM) data
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distribution drawings, wiring diagrams junction box layout and cable assembly drawings. Develop design packages to procure control system enclosures or racks from fabrication shops. Be the liaison between
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the Research Reactors Division is responsible for the High Flux Isotope Reactor configuration management program. This group also provides technical support to operations and maintenance and performs system
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vulnerabilities or weaknesses. Scalability and Cluster Computing Design distributed systems that support high-throughput simulations and stress-testing of AI systems under adversarial conditions. Implement cluster
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-scale distributed geospatial workflows that require GPU-based high-performance computing (HPC) across multiple network domains. Major Duties and Responsibilities: RSG is seeking a senior researcher who
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registration updates, and supplier questionnaires. System Integration & Reporting: Assist the ORNL SAP Applications team with integration issues due to data discrepancies and generate/review/distribute
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: The design and analysis of computational methods that accelerate AI/ML when applied to large scientific data sets; Energy efficient physics-aware algorithms, capable of distributed learning on high performance