Postdoctoral Research Fellow Position in Causal Modeling and Discovery
One of the most challenging and important problems in AI is the development of effective computational methods for causal discovery from a combination of observational data, experimental data, and background knowledge. Biomedicine is a rich domain in which to explore new causal discovery methods, because of the availability of large datasets and the importance of improving human health. The University of Pittsburgh is recruiting for a postdoctoral research fellow in biomedical informatics (NIH funded) to develop and evaluate new methods for causal modeling and discovery as applied to biomedicine. Current postdoctoral stipend levels for this position can be found at http://www.dbmi.pitt.edu/node/311 Requirements: * A doctoral degree in Computer Science, Statistics, Biomedical Informatics, or a related discipline. * An interest in applying computational methods to biomedical problems to improve human health. * NIH fellowship eligibility requires US citizenship or permanent resident status. Application procedure: Please go to https://apply.dbmi.pitt.edu/ and complete the online application. You will need to include the following information: * Curriculum vitae * A description of your research interests * Contact information for three references * One writing sample -- preferably a published or accepted paper Note: The application fee will be waived. For questions about the research aspects of this position, please contact Greg Cooper at g...@pitt.edu<mailto:g...@pitt.edu> For questions about the administrative aspects of the position and the application process, please contact Toni Porterfield at tl...@pitt.edu<mailto:tl...@pitt.edu> The University of Pittsburgh is an affirmative action, equal opportunity employer.
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