Stanford Postdoc in Machine Learning and Public Policy

[Christine Tsang]

Professors Daniel Ho and Jacob Goldin invite applications for a postdoctoral scholar focused on the intersection of machine learning, statistics, and public policy. The work would focus on a high-impact collaboration with the Internal Revenue Service to build a more effective and equitable tax system. This is a 2-year position, with the potential of renewal. Some or all of the work may be conducted remotely. For more information, see the RegLab website.

We are searching for outstanding individuals with strong research backgrounds. Experience with research in one or more of the following areas is desirable: machine learning, algorithmic fairness, sequential decision making (e.g., active learning). A Ph.D. degree in Computer Science, Economics, Statistics or a related field is required.

Successful candidates should be prepared to send two letters of reference from your previous research supervisors and collaborators on request. The review of applications will begin immediately and will continue until the position is filled.

Apply online at: https://forms.gle/iVz8HVCxJeUVPNxQ9

Stanford University is an affirmative action and equal opportunity employer, committed to increasing the diversity of its workforce.

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Christine Tsang

Executive Director | Regulation, Evaluation, and Governance Lab (RegLab)

Stanford Law School | 559 Nathan Abbott Way | Stanford, CA 94305