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Program (DOE IP) to advance the chemical processing of unique f-element isotopes, including Cf-252, Bk-249, Es-254, Fm-257, and Pm-147. A core focus of the DOE IP is to improve and develop novel chemical
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Requisition Id 15813 Overview: We are seeking a highly motivated postdoctoral researcher with a strong background in sensor integration, data acquisition, and in situ process monitoring
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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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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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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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respectful workplace – in how we treat one another, work together, and measure success. Required Qualifications: PhD in Nuclear engineering, computer science, applied mathematics, or a related field completed
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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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-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
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opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success Basic Qualifications: A Ph.D. in Condensed Mather Physics, Materials Science and Engineering, or