The newly-founded NSF Institute for Foundations of Data Science (IFDS) at the University of Washington (UW), Seattle, is seeking applications for one or more Postdoctoral Fellow positions. This is an ideal position for candidates interested in interdisciplinary research under the supervision of at least two faculty members of IFDS, which brings together researchers at the interface of mathematics, statistics, theoretical computer science, and electrical engineering. IFDS at UW operates in partnership with groups at the University of Wisconsin-Madison, the University of California at Santa Cruz, and the University of Chicago, and is supported by the NSF TRIPODS program. A unique benefit is the rich set of collaborative opportunities available, allowing truly interdisciplinary training. Initial appointment is for one year, with the possibility of renewal. Appropriate travel funds will be provided.
The ideal candidate will have a PhD in computer science, statistics, mathematics, engineering or a related field, with expertise in data science and machine learning. Desirable qualities include the ability to work effectively both independently and in a team, good communication skills, and a record of interdisciplinary collaborations.
Interested applicants should send a statement of research interests, CV, and two letters of reference to the address ifds.UW.firstname.lastname@example.org, with the subject line “IFDS Postdoc Application.” In their cover letter, applicants should make sure to indicate potential faculty mentors (primary and secondary) at UW IFDS (see here for a list of current members). Full consideration will be given to applications received by January 20th, 2021, but applications are accepted until positions are filled. The expected start date is in summer 2021, but earlier start dates can be considered. For questions regarding the position, please contact the IFDS Director Prof. Maryam Fazel at email@example.com.
The University of Washington is an Affirmative Action, Equal Opportunity Employer and applications are strongly encouraged from women and underrepresented groups.