We are seeking up to two exceptional researchers to work on calculation of Fundamental Limits of Learning for High Dimensional, Purely High dimensional data, Sparse Data, and the Design of Limit Achieving Algorithms  to work with Vahid Tarokh  at the  Information Initiative at Duke  https://bigdata.duke.edu/ .  

Applicants are expected to hold a Ph.D. degree in Math, Stat, EE, CS, Physics or a closely related field. We are seeking mathematically sophisticated and intellectually curious researchers at an early stage of their scholarly careers.

The successful candidate is expected to have a background and familiarity with some of these topics: Model Matching, Expert Systems, Information Theory, and Online Optimization Methods. Familiarity with at least one programming language is required.

This effort has been funded by a generous grant from DARPA. The successful applicants are expected in frequent meetings, teleconferences, and to lead other postdocs and graduate students in the DARPA HACKATHON Challenge. The original appointment period is for one year (beginning April 1, 2018), but may be extended.

Applicants are asked to submit (a) cover letter; (b) a vitae; and (c) a research statement describing current and past research (two page maximum). The applicant should request at least three letters of recommendation, but no more than five. These letters should be uploaded, by their authors, at  mathjobs.org . Applicants are encouraged to submit all of their materials electronically at this site. Applicants who do not have internet access may mail their materials to: Appointments Committee Department of Mathematics, Box 90320, Duke University Durham, NC 27708-­0320.

Applications received by June 1, 2018 will be guaranteed full consideration; early application is advisable.

Duke University seeks to build a diverse faculty: women and under-represented minorities are encouraged to apply. Duke University is an Affirmative Action/Equal Opportunity Employer committed to providing employment opportunity without regard to an individual's age, color, disability, genetic information, gender, gender identity, national origin, race, religion, sexual orientation, or veteran status.