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Position TitlePostdoctoral researcher
The University of Chicago is an urban research university that has driven new ways of thinking since 1890. Our commitment to free and open inquiry draws inspired scholars to our global campuses, where ideas are born that challenge and change the world.
The University of Chicago Booth School of Business is the second-oldest school in the U.S. and second to none when it comes to influencing business education and business practices. Since 1898, the school has produced ideas and leaders that shape the world of business. The Econometrics and Statistics group is diverse and rapidly growing, with 12 full-time faculty working in diverse areas such as statistical machine learning, causal inference, Bayesian statistics, financial econometrics, and forecasting.
Applications are invited for a postdoctoral researcher working under the supervision of Prof. Bryon Aragam in statistics, machine learning, and/or optimization within the Econometrics and Statistics group at the Booth School of Business of the University of Chicago.
Potential candidates should have a background in statistics and machine learning, for example nonconvex optimization, nonparametric statistics, and/or learning theory. Applications of interest include personalization and graphical models, but may also depend on the candidate’s individual research interests. There will be an emphasis on theoretical/mathematical problems as well as computational/applied work, with a particular focus on problems at the intersection.
The candidate should have a recent Ph.D. degree (or all-but-dissertation) in statistics, computer science, mathematics, or a related area, and should be proficient in programming in Python or R.