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Field
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record and record of completing projects in a timely manner ● Excellent quantitative skills, especially statistical ● Experience processing, manipulating, and modeling data ● Demonstrated proficiency with
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management machine learning, distributed computing, and resource optimization leveraging the unique computational resources available at ORNL, including the Frontier supercomputer—the world's first exascale
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principals to problem solve work. ● Ability to maintain detailed records of experiments and outcomes. ● Ability to quickly learn and master computer programs, databases, and scientific applications
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approaches for important questions in neuroscience. We have multiple current and incoming NIH projects to establish cellular cell type architecture maps of mammalian brains using mice as an animal model. Three
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well as artificial intelligence and machine learning techniques (AI/ML) with emphasis on electronic properties (charge and spin) of a range of materials important to the DOE mission, including the materials classes
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
computational materials Excellent written and verbal communication skills Preferred Qualifications, Competencies, and Experience Experience in the development and implementation of machine learning models Special
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conduct groundbreaking translational research centered on human disease biology. Additionally, we utilize ex vivo multicellular organoid culture models and in vivo disease modeling in mice to enhance our
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
Engineering stresses the application of science and engineering, mathematical analysis, and computer techniques to biomedical problems. The research and entrepreneurship of the faculty, students, and staff aim
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computing, and parallel programming paradigms Strong publication record in reputable avenues Strong writing skills Strong skills in model development, data analytics, machine learning, and visualization
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analytics, and machine learning, the Grid Interactive Controls group delves deeply into understanding intricate grid-edge operations. Researchers are dedicated to laying the groundwork for optimal X2G