Assistant/Associate/Full Professor, Economics and Computer Science


  • Professional
  • New Haven, CT
  • Posted 2 months ago
  • Expires on: December 31, 2022

Yale
Computer Science

As part of a new interdisciplinary initiative between Computer Science and Economics, we invite applications at the Assistant/Associate/Full Professor level for joint appointment or primary/secondary appointments in the two departments. We are particularly interested in candidates with interest in algorithms, blockchains and cryptocurrency, causal inference, game theory, learning, machine learning, market design, and networks, but all subjects at the intersection of these two scientific disciplines are invited to apply.

Future faculty members will be affiliated with the Cowles Foundation for Research in Economics and a newly created Center for Algorithms, Data, and Market Design. Appointees are expected to teach at undergraduate and graduate levels and to engage in research.

For appointments at the starting Assistant Professor level, applicants must show promise of outstanding scholarly achievement and the Ph.D., or equivalent degree, must be completed, or its completion should be expected within the first year of appointment. Applicants for more advanced Assistant, Associate, and full Professorships must have a demonstrated record of scholarly achievement.

Please electronically submit a letter of interest, CV, job market paper, and, for non-tenured candidates, 3 confidential letters of recommendation to http://apply.interfolio.com/115066 before November 15, 2022.

A review of all applications will begin December 1, 2022, with an anticipated start date of July 1, 2023. If you have specific questions about the position for which you are applying, please contact Nicole Whitcher (nicole.whitcher@yale.edu).

Yale University is an Affirmative Action/Equal Opportunity employer. Yale values diversity among its students, staff, and faculty and strongly welcomes applications from women, persons with disabilities, protected veterans, and underrepresented minorities.