Position Description: Two Post-doctoral research assistantships are available to work on Distributed (Nonconvex) Optimization, statistical learning or Information Processing in the School of Industrial Engineering at Purdue University. The research team has several ongoing research projects (supported by NSF and ONR) in the areas of i) distributed (stochastic) optimization for (nonconvex) large-scale systems; ii) computational big-data analytics over networks; and iii) Signal processing over graphs.

Qualifications: We are seeking candidates that have a record of scholarship under the rubric of "distributed (nonconvex) optimization" and/or data analytics/processing (e.g., analysis of large datasets, machine learning, multi-agent optimization, network analysis) who have the potential to publish in premier journals. Candidates may come from different backgrounds, such as operation research, electrical engineering, computer science or a related field, but preference will be given to candidates with a strong background in optimization and machine learning. All applicants must have strong mathematical and computing skills. Prior knowledge in stochastic programming is a plus. 

Application: The positions are available immediately and offered for one-year terms, subject to renewal based on performance. Salary is competitive and commensurate with rank and qualifications. Review of applications will begin immediately and continue until the positions are filled.
Applications should include a cover letter and (a) full curriculum vitae, (b) a brief statement of research interests, (c) evidence of excellence in research, (d) the names and contact info of three references, and (e) two representative papers related to the topics of distributed optimization and big-data analytics.  
Please send all materials to: Dr. Gesualdo Scutari  Please use as subject of your email “Application for the post-doc position”