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Postdoctoral Position on Optimization and Information Theory
Profs. Angelia Nedich and Lalitha Sankar in the School of Electrical, Computer, and Energy Engineering at Arizona State University are looking for a postdoctoral fellow to work on the topics of optimization and learning theory in the context of generative adversarial networks (GANs). An ideal candidate will have a PhD in EE, CS or IE with a strong background in optimization, information theory, and/or statistical learning theory.

Profs. Angelia Nedich and Lalitha Sankar in the School of Electrical, Computer, and Energy Engineering at Arizona State University are looking for a postdoctoral fellow to work on the topics of optimization and learning theory in the context of generative adversarial networks (GANs). An ideal candidate will have a PhD in EE, CS or IE with a strong background in optimization, information theory, and/or statistical learning theory. Familiarity with functional analysis is desirable as the work will explore such connections. 

Interested candidates should contact both Profs. Nedich and Sankar at [email protected] ²¹²Ô»åÌý[email protected] with a CV (including at least three references) and a list of publications. Ideal applicant will work with both faculty very closely and address fundamental technical challenges in optimizing and tuning GANs. This position is funded by the National Science Foundation and Simon Foundation’s Mathematics of Deep Learning program.

Arizona State University is the largest public school in the United States. Its ECEE department is ranked in the top 25 in the country ²¹²Ô»åÌýits faculty includes leading researchers in the areas of optimization, communications, signal processing, information theory, and statistical learning theory.

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