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-scale scientific data. Publishing research in leading peer-reviewed journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources
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journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources. Collaboration with domain scientists for demonstration and validation
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compliance, reproducibility, and interoperability across scientific domains. By improving data readiness processes, this role will amplify the potential of AI-driven discovery in areas such as high energy
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, finite volume, and machine learning to solve challenging real-world problems related to structural materials and advanced manufacturing processes. The successful candidate will have experience with
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(ORNL). This position will focus on the development, characterization, and application of engineered nanoparticles for medical isotope systems, including technologies relevant to isotope processing
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data science to develop new methodologies for assessing and improving the quality of components fabricated using advanced manufacturing processes. This position resides in the Manufacturing Systems
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environment. The successful candidate will develop and apply advanced machine learning techniques—including multimodal AI, computer vision, and large language models—to complex scientific and engineering
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process-based modeling of hydrologic or land surface processes. The WSMG group develops advanced surface/subsurface integrated hydrologic and reactive transport models, works with other groups to compare
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mathematical representations of quantum states and processes, develop algorithms, and produce open-source scientific software Write peer-reviewed scientific articles and present research findings at major
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-class S&T products for sensitive national security missions. The selected candidate will support research efforts in signal processing and analysis, with an emphasis on the development of novel algorithms