[TYPES/announce] Postdoctoral Researcher in Programming Languages and Machine Learning at Penn State

[Danfeng Zhang]

Applications are invited for a 1-2 year full-time Postdoctoral Research position in differential privacy in the Computer Science & Engineering Department at Penn State. The position involves statistical and/or formal verification of differentially private algorithms, including the development of tools and theories for analyzing source code using programming languages and statistical methods.

As differential privacy gains increased acceptance in industry and government agencies as a methodology for protecting privacy, there is an increased need for tools for verifying correct code and detecting bugs in incorrect code. In this project, we explore program verification techniques (e.g., type systems) to prove differential privacy for sophisticated algorithms, as well as methods for generating counterexamples when the proof fails. See below for our recent papers in this direction:
CCS’18: http://www.cse.psu.edu/~dbz5017/pub/ccs18.pdf
POPL’17: http://www.cse.psu.edu/~dbz5017/pub/popl17.pdf

Please apply at https://psu.jobs/job/83998 <https://psu.jobs/job/83998>
An applicant should possess a doctoral degree in Computer Science or Statistics and have strong background in one or more of the following areas: machine learning, programming languages, non-parametric statistics, differential privacy. The candidate must have an excellent track record of original research and the ability to work as part of a team. The postdoc will be provided with competitive salary and employment benefits. The initial appointment will be for one year, with an option to renew for a second year. 

Inquiries about the position should be directed to Daniel Kifer and Danfeng Zhang at CMLA at psu.edu <mailto:CMLA at psu.edu>. Applicants should upload a resume with at least two names of references. Applications will be reviewed until the position is filled.
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://LISTS.SEAS.UPENN.EDU/pipermail/types-announce/attachments/20181220/ccfd4eaf/attachment.html>