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Job Posting: Tenure Track Faculty Position

Improving Public Policy Through Data and Behavioral Science
RECRUITMENT PERIOD

Open August 18th, 2016 through October 17th, 2016


DESCRIPTION

The Richard & Rhoda Goldman School of Public Policy at the University of California, Berkeley invites applications for a tenure-track appointment to the faculty at the Assistant Professor rank, with an expected start date of July 1, 2017.

The Goldman School of Public Policy seeks applications from researchers who use data and behavioral science with an intent to solve pressing social problems, improve public policy, and improve government performance. We anticipate that these scholars will study some combination of public sector management, bureaucracy, social and/or political psychology, organizational behavior, and data science, and we expect a demonstrated interest in using research to advance public policy. We are looking for intellectually rigorous and exciting candidates who can engage with a diverse faculty and can teach innovative courses to our graduate students.

The Goldman School encourages applications from individuals of any scholarly discipline whose research focuses on these questions, including those in Behavioral Economics, Psychology, Political Science, Sociology, and Law, or data scientists with roots in Computer Science, Schools of Information, Economics and other fields. Preference will be given to scholars who have demonstrated the potential to make significant research contributions and who have a clear interest in teaching in the core of the School’s Masters in Public Policy professional program. Public sector experience is also desirable.

Minimum Qualifications: The minimum qualification required to be considered an applicant for this position is a Ph.D. or equivalent degree, or the completion of all degree requirements (except the dissertation) at the time of application.

Additional requirements: Candidates must have been awarded the Ph.D. or equivalent degree by June 30, 2017.

Read the full posting here.