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with developing AI/ML workflows and integrating them into software projects. Developing or contributing to large, complex software systems. Scientific data visualization and/or scientific data analysis
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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
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into impactful energy, environmental, and national security solutions. The Geospatial Data Modelling Group within the Human Dynamics Section, part of the Geospatial Science and Human Security Division at ORNL, is
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on either a large-scale scientific research and development project or large scientific instrument project. Oak Ridge National Laboratory (ORNL) is the largest US Department of Energy science and energy
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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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electromagnetic transient models for bulk power systems, synchronous generators, large loads, etc. Develop simulation algorithms that enable large-scale simulations and/or AI/ML experience for intelligence in
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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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analysis of large-scale 2D/3D scientific data. This position resides in the Data Visualization Group in the Data and AI Systems Section, Computer Science and Mathematics Division, Computing and Computational
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materials. In this role, you will develop and apply methods that integrate physics‑guided image correction with intelligent (AI/ML‑enabled) data‑acquisition strategies. Key objectives include (1) implementing
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Postdoctoral Research Associate who will focus on creating innovative artificial intelligence algorithms for the trusted visualization of large-scale 3D scientific data. This position resides in the Data