Postdoctoral Fellowship in Non-Commutative Information Theory and Processing, Duke University

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The postdoc is at Duke University under the supervision of Prof. Vahid Tarokh.

We are seeking an exceptional researcher to work with Vahid Tarokh at the Information Initiative at Duke on foundations of Non-Commutative Information Theory, and the Design of Algorithms for the Processing of Multimodal Data based on these theoretical findings.

Applicants are expected to hold a Ph.D. degree in EE, Math, Stat, 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 will have a background and familiarity with probabilistic techniques and applications. Knowledge of free probability theory and random matrix theory is highly desired.

This effort is funded by a generous grant from US Army Research Office (ARO). The original appointment period is for one year, but may be extended for up to three years. Position can begin as early as March 1, 2018.

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 Feb 28, 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.