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unique opportunity to engage in transformational research that advances the development of AI-ready scientific data, optimized workflows, and distributed intelligence across the computing continuum. In
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simulation and flood inundation modeling. River basin planning and operations modeling, including reservoir simulation and optimization. Hydrodynamic modeling of water temperature and quality constituents
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and machine-learning-driven optimization frameworks for polymer composite manufacturing processes. This position resides in the Composites Innovation Group in the Manufacturing Science Division (MSD
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). The Emerging Technology Computational Group facilitates ORNL goals through HPC systems engineering, integration, and support for the research community at ORNL. By providing design, deployment, optimization
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. Masters and PhD degree holders in the same fields of study are also encouraged to apply: Masters’ holders should have a minimum of eight (8) to ten (10) years of relevant and aligned experience. PhD holders
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methods of interest include adaptive meshing for design/shape optimization as well as solution optimization. In addition to software development/application, you will also engage with the broader community
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methods of interest include adaptive meshing for design/shape optimization as well as solution optimization. In addition to software development/application, you will also engage with the broader community
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energy, science, and technology. Leveraging a 75+ year UT–ORNL collaboration, UT-ORII supports convergent research, joint institutes, interdisciplinary PhD programs, and leadership development
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optimize process performance by integrating analytical feedback into the continuous improvement of synthesis, purification, dispensing and gas conversion methods. Prepare reports, presentations, and operator
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computing solutions to efficiently scale testing environments supporting large datasets and high-performance AI workloads. Optimize resource allocation for simultaneous testing tasks and real-time tracking