Research Interest. Our group is interested in the analysis of decision theory, in particular the extension to non-commutative probabilities, deep reinforcement learning, textual analysis for the application to belief revision.
Description: 100%: You will lead a new research project in developing one or multiple computational algorithms for analyzing decision processes starting with a decision theoretic approach based on non commutative probability theory. You will consider applications to social learning and empirical tests of the models developed using textual analysis; the appropriate theoretical and statistical tools will have to be developed for such analysis. You will present the progress of the project orally in intramural, national, and international meetings and conferences. You will contribute to writing scientific manuscripts and gran progress reports.
More information about the position and research group is available at: http://users.econ.umn.edu/~rusti001/Announcements/PostDoc.pdf
1. Ph.D., or equivalent degree in Bioinformatics, Biostatistics, Computer Sciences, Statistics, Economics or Mathematics.
2. Demonstrated research experience as evidenced by a strong publication record.
3. Experience in developing statistical and/or machine-learning algorithms.
4. Strong computational skills covering Python and R programming languages.
1. Experience in textual analysis.
2. Experience in constructing deep artificial neural network models
3. Strong problem-solving and data handling skills.
4. Good communication and writing skills
Internal Number: 339330
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