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Postdoc position in method development in human statistical genetics, with a focus on classificat...
basic and applied research within plant, livestock and human quantitative genetics. Our focus areas include quantitative genetics, artificial intelligence applied to agriculture and precision medicine
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postdocs, tenure-track positions, tenured positions, and positions for distinguished professorship. The Group of Uncertainty Artificial Intelligence is one of the core branches of the College of Mathematical
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capabilities to aid in the predictive engineering of biological systems, such as proteins, as part of the NIST Engineering Biology Program. Develop artificial intelligence and machine learning analysis pipelines
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 5 hours ago
simulations. This role will support innovative research at the intersection of Artificial Intelligence, Computational Biophysics and Chemistry, and Drug Discovery. The successful candidate will contribute
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, software and data engineering, data mining, machine learning, and Artificial Intelligence. Qualified candidates are invited to submit their applications through the web portal available at https
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are now seeking postdocs to join the Institute and its wider research community. We welcome applications across all areas of machine learning, artificial intelligence, and related fields. Your research can
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AI-based Cybersecurity Automation. We are seeking a highly motivated researcher to advance research on cybersecurity automation through artificial intelligence applications, with an emphasis
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. The postdoc will be supervised by Giulio Biroli and involved in the activities of the Paris Research Institute on Artificial Intelligence (PRAIRIE-PSAI), the CFM-ENS Data Science Chair and will have link with
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basic and applied research within plant, livestock and human quantitative genetics. Our focus areas include quantitative genetics, artificial intelligence applied to agriculture and precision medicine
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areas include the development of interpretable and trustworthy algorithms for Scientific Artificial Intelligence and active learning, integrating FAIR data management practices throughout the research