We seek candidates with a proven background and experience in one or several of the following areas: Markov Chain Monte Carlo, high-dimensional Bayesian methods and asymptotics, Markov chains theory, non-smooth optimization. The project offers the opportunity to work on cutting-edge methods and theory in high-dimensional Bayesian inference. This is a one-year position that is renewable depending on availability of funding and satisfactory performance.

Qualifications required:

A PhD in statistics, mathematics, or a related quantitative field is required.
Additional qualifications include programming skills in R and Matlab. Programming experience in C/C++ is a plus.

Applications Procedure:

Submit a cover letter, CV, a research statement, academic transcripts, and contact information of two references to: yvesa@umich.edu

Additional details:

This is a one-year position that is renewable once. Competitive salary. The position has a teaching component that is partially negotiable. Application review is ongoing and will continue until the position is filled. The offer is contingent on funding approval. All offers of employment at the University of Michigan are contingent upon the candidate successfully passing a background check.

For more information about this posting, please contact: Yves Atchade, yvesa@umich.edu