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Field
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Associate who will focus on creating innovative uncertainty quantification and visualization algorithms that enable trusted visual representation and analysis of large-scale 2D/3D scientific data
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on creating innovative artificial intelligence algorithms for the trusted visualization of large-scale 3D scientific data. This position resides in the Data Visualization Group in the Data and AI Systems
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, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn't imagine the future, we invent it. If you're passionate about joining a community that challenges the
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-driven techniques for the generation and exploration of complex, large-scale scientific data. Publishing research in leading peer-reviewed journals and conferences. Researching and developing parallel
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behavioral plasticity across generations. More information about the research group is provided at https://abentz.wixsite.com/bentzlab . The position is funded by an active NSF grant. Applicants are expected
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and cleaning of worksite. Analyzes research data and summarizes results. Writes and may contribute to research papers, articles, and publications. Mentoring May assist, mentor, and/or supervise student
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engineering. We seek candidates with an interest in participating in large multi-PI grants and with collaborative research experiences, experience writing proposals for user-facilities, and/or experience
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of Medicaid, Medicare, and private insurance data and the application of social science theory and advanced statistical techniques in large data sets. Statement Posting Details Special Instructions
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. Related research experience in atmospheric observation and data analyses. Preferred Qualifications: Ph.D. (or foreign equivalent) in Atmospheric Sciences, Engineering, Physics, or a closely related field
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. The research focus will be on developing and applying first-principles-based and data-driven computational methods to understand multiscale processes and to accelerate chemical discoveries for renewable energy