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seeking applications for a full-time post-doctoral associate. We are conducting several large randomized clinical trials, cohort studies, and surveillance programs, in India, Latin America, and the
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space-based imaging required. Desired Qualifications: Significant software development experience and work with large astronomical data sets, including research publications resulting from such data sets
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Details Posted: Unknown Location: Salary: Summary: Summary here. Details Posted: 21-Nov-25 Location: Chapel Hill, North Carolina Categories: Academic/Faculty Computer/Information Sciences Internal
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including issues preferably related but not limited to the use of automation, decision theory, organizational theory and/or workforce social questions. - Experience in data science and working with large
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of development, investment decision making, financing, and construction activities that are associated with the disciplined allocation of capital to large-scale infrastructure projects by corporations
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solutions for large-scale scientific data models in federated learning environments. You will advance privacy-preserving machine learning by developing efficient techniques that maintain robust privacy
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at the graduate level, programming. Analysis group leadership, student mentorship. Data analysis for HEP. ML reconstruction, Geant4, software development, statistics. Position 2: Postdoctoral Research Associate
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Simulation & Data Processing: Use and extension of Allpix2 and TCAD-based simulation tools. Generation of large simulation datasets for algorithm training and validation. Integration of simulation with
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subject to continuing based on the availability of funding. The work will focus on the production of large polarimetric maps from the SOFIA data. The position is planned for two years, with a possible
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 1 month ago
integration, modeling cancer vulnerabilities, designing tools for data interpretation and visualization, and/or training foundation models and large language models (LLMs) for diverse biomedical applications