The NYU Center for Data Science is pleased to invite applications for its Moore-Sloan Data Science Fellows. The positions are a prominent feature of the Moore-Sloan Data Science Environment at NYU and Data Science Fellows will be expected to work at the boundaries between the data-science methods and the sciences. These positions are part of a multi-institutional effort funded in part by a generous grant from the Moore and Sloan Foundations. As part of the multi-institutional effort, fellows will be encouraged to develop collaborations with partners at the University of California, Berkeley, and the University of Washington.

Fellows are expected to lead independent, original research programs with impact in one or more scientific domains (natural science or social science) and in one or more methodological domains (computer science, statistics, and applied mathematics).   Ideal candidates will have earned a PhD in one of these disciplines with experience in one of the other areas (for instance a PhD in machine learning with applications to biology or a PhD in politics with extensive use of statistical inference).  Superior candidates will bring a research agenda that can take advantage of the unique intellectual opportunities afforded by NYU, and will have experience in working with researchers across different fields.

Appointments will be initially for two years, with an expectation of renewal for a third on satisfactory performance. Fellowships will be offered competitive salary and benefits, with funds to support research and travel.  There is some flexibility about start date, but January 1, 2017 is expected.

Fellowship applicants should send a curriculum vitae, list of publications, and brief statement of research interests (no longer than 4 pages) to and also arrange to have three letters of recommendation sent by November 1, 2016.  The statement of research interests should mention the names of one or more faculty members associated with the NYU Center for Data Science who would have substantial intellectual overlap with the applicant’s interests and likely program of research.  More information about the program can be found at