Postdoctoral Principal Researcher in Machine Learning at University of Chicago Booth School of Business

[mkolar]

Institution: University of Chicago Booth School of Business 

Position Title: Postdoctoral Principal Researcher in Machine Learning

Contact: Mladen Kolar, Mladen...@ChicagoBooth.edu
http://mkolar.coffeejunkies.org/

More info:

https://www.chicagobooth.edu/-/media/faculty/research-professional-program/job-ads/kolar_postdoctoral-principal-researcher-in-machine-learning_v3.pdf

General Summary:

The Econometrics and Statistics group at the Booth School of Business of the University of Chicago invites applications for a postdoctoral Principal Researcher (PR). The PR will be working under the supervision of Dr. Mladen Kolar, but will have ample opportunities to collaborate with other members on the campus. The PR will be working on a project in one of the following areas: high-dimensional inference, probabilistic graphical models, federated learning, or reinforcement learning in nonstationary environments. The specifics of the project will depend on the PR’s interest and prior experience. The PR will also be given ample time to pursue their own research agenda and prepare for the academic job market. 

This position is structured to prepare the PR to transition to a faculty position at a major research institution. The PR will conduct research, develop release-quality code supporting new methodology, and perform benchmarking comparisons in synthetic and real-data exercises. The PR will also create and edit written materials like research documentation for potential publication, and talks and posters for research conferences. 

The PR is expected to be independently motivated to execute high-quality research in a collaborative environment. Success will be measured by the quality of deliverables (code, papers, presentations) as judged by mentors and by the community at large. The goal is to gain valuable experience through challenging projects along several dimensions: mathematics, methodology, implementation, experimentation. 

A successful candidate should have a PhD in machine learning, statistics, computer science, econometrics, industrial engineering, or a related field. They will ideally have familiarity with highdimensional inference, probabilistic graphical models, federated learning, or reinforcement learning, and related machine learning and statistics literatures. They should have experience developing optimization procedures, and a command of high-level programming languages (e.g., Julia, Python, Matlab, R) and compiled language supporting customizable data structures (C, C++). Comfort in Unix (e.g., Linux or OSX Terminal) and LaTeX are preferred. This position will require the ability to work independently and in teams, and to communicate and organize well. 

Chicago Booth is dedicated to creating an environment where people of different backgrounds, broadly defined, feel valued and where their ideas and contributions can flourish. Chicago Booth welcomes applications from women, persons with disabilities, veterans, and under-represented groups. If you need an accommodation to apply to a Chicago Booth Research Staff Program, or if there are other factors impacting your ability to apply to an open position, please email ResearchPr...@lists.chicagobooth.edu.