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 deadline for the first round is 5pm PST on Sunday, November 28, 2021. The deadline for the second round is 5pm PST on Sunday, January 30, 2022. Applications will be evaluated on a rolling basis and preference will be given to first-round applicants.

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