Open date: January 27th, 2021
Last review date: Tuesday, Mar 30, 2021 at 11:59pm (Pacific Time)
Applications received after this date will be reviewed by the search committee if the position has not yet been filled.
Final date: Monday, Jan 24, 2022 at 11:59pm (Pacific Time)
Applications will continue to be accepted until this date, but those received after the review date will only be considered if the position has not yet been filled.
This position is for a Postdoctoral Scholar position. Applicants will broadly work on algorithmic fairness in healthcare under the supervision of Prof. Berk Ustun at UCSD and Prof. Flavio Calmon at Harvard SEAS. There is flexibility to work on theory or method development related to topics like the fairness in personalization, the use of protected attributes.
Applicants must have a PhD in Computer Science, Electrical Engineering, Mathematics, Statistics, Data Science, Bioinformatics (or a related discipline) or be confident of its completion by the time of the start of this position. Preferred to have experience in machine learning, an interest in algorithmic fairness, and expertise in at least one of the following areas: optimization, statistics, causal inference, and information theory. Prior experience in healthcare is preferred, as is an ability to code in Python. Candidate with a commitment to support diversity, equity, and inclusion in an academic setting are preferred.
This is a joint postdoctoral fellowship at Harvard and UCSD with a lot of flexibility. You may choose to reside in Boston or San Diego, or split your time between the cities (e.g., spend the academic year in San Diego and the summers in Boston). The earliest start date is September 2021, though a later start would be fine. The position is funded for two years, with a potential extension for a third year.
We will review applications starting on February 15, 2021. Applications will be accepted after this deadline until the position is filled. Applications must be submitted to the UCSD on-line application collection system, AP-On-Line Recruit, at: https://apol-recruit.ucsd.edu/apply/JPF02646
We are looking for applicants with demonstrably strong research skills, ideally, with publications in top venues in machine learning and data mining. Research experience in reinforcement learning and/or interpretable and fair ML is strongly preferred.
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
Flavio Calmon is an Assistant Professor of Electrical Engineering at Harvard's John A. Paulson School of Engineering and Applied Sciences. Before joining Harvard, he was the inaugural data science for social good post-doctoral fellow at IBM Research in Yorktown Heights, New York. He received his Ph.D. in Electrical Engineering and Computer Science at MIT. His main research interests are information theory, inference, and statistics, with applications to fairness, privacy, machine learning, and communications engineering. Prof. Calmon has received the NSF CAREER award, the Google Research Faculty Award, the Amazon Research Award, the IBM Open Collaborative Research Award, Harvard's Lemann Brazil Research Fund Award, and a Harvard Commendation for "Extraordinary Teaching in Extraordinary Times" for his undergraduate signal processing course. For more information, please visit http://people.seas.harvard.edu/~flavio/
Berk Ustun is an incoming Assistant Professor at the Halıcıoğlu Data Science Institute at UC San Diego. His research lies at the intersection of machine learning, optimization, and human-centered design. Specifically, he is interested in developing methods to promote the adoption and responsible use of machine learning in medicine, consumer finance, and criminal justice. Prior to his appointment at UCSD, Ustun held research positions at Google AI and the Center for Research on Computation and Society at Harvard. He received a PhD in Electrical Engineering and Computer Science from MIT, an MS in Computation for Design and Optimization from MIT, and BS degrees in Operations Research and Economics from UC Berkeley. For more information, please visit https://www.berkustun.com
Curriculum Vitae - Your most recently updated C.V.
Statement of Research
Statement of Contributions to Diversity - Applicants should summarize their past or potential contributions to diversity. See our Faculty Equity site for more information.
Copies of Minimum of 2 Publications
- 3-5 letters of reference required