Current Openings

Postdoctoral Position in How Programming Languages Shape Thought

The Knowledge Lab at the University of Chicago seeks to hire outstanding candidates for a postdoctoral research project with support from the Sloan Foundation to explore the degree to which programming languages and data science environments shape how individuals, groups and communities “think”—how they construct code, analyze data and solve computational problems together. The project, titled “The Impact of Programming Languages and Datascience Frameworkson Thinking, Software, and Science” is inspired by the longstanding Sapir/Whorf Hypothesis that natural languages influence how speakers think, which has garnered new evidence with computational methods and large-scale language data. This project involves analysis of all public GitHub and other code repositories with statistical and machine learning approaches that generate insights that link programming language properties to individual and group behavior to coding and analytical outputs. Based on insights from these large-scale analyses and ongoing surveys of programming communities, we will generate programming experiments (e.g., with the Jupyter interface) to test whether discovered associations are causal—whether changing languages can predictably improve the efficiency, collaboration, and creativity of coders and coding communities.

Postdoctoral candidates will design and conduct independent research, in collaboration with UChicago Professor and Knowledge Lab Director James Evans, and Gary Lupyan, a computational psychologist from the University of Wisconsin-Madison. Candidates much hold a PhD in Computer Science, or have substantial computational and data science background and a Ph.D. in Statistics, Applied Math, Sociology or another Social Science, Linguistics, Informatics, (statistical) Physics or a related field, and a strong publishing background.

Specifically, the successful candidate(s) will be responsible for managing and analyzing a massive collection of version controlled source code with Machine Learning (ML) and Natural language Processing (NLP) techniques. Candidates must understand and will need to maintain long running web scraping tasks, via APIs and HTML parsing and have knowledge regarding the state of the art in NLP (specifically neural language models, context free grammars and auto encoders), which they will extend to new domains, primarily programming code. This development of new techniques for understanding source code will likely benefit from knowledge of compiler design, static analysis, complex systems and network analysis. Candidates must have knowledge of Python and experience running large scale computational tasks on UNIX systems. Proficiency in multiple other programming languages, including a functional language, would be a benefit. Positions could begin anytime within the coming year, and as early as immediately.

To apply, please send CV and names for letters from at least two references to Candice Lewis, cllewis@uchicago.edu.

Postdoctoral Position in Expanding Machine Learning beyond Prediction to Explanation, Intervention
The Knowledge Lab at the University of Chicago seeks to hire 1-2 outstanding candidates for a postdoctoral research opportunity with support from DARPA to extend the limits of machine learning from predicting social systems to explaining causal factors in those systems to intervening in them. This is in association with the “Ground Truth” program at DARPA. Other teams will generate reasonable agent-based models of diverse social systems, and our task is to build automated, analytical techniques that induce the “ground truth” or structure of the model and program used to generate them. We will also predict future instances of these social systems, and propose desirable and pragmatic interventions in them. Our team, the “Social MIND (Machine Inference for Novel Discovery)”, is exploring approaches that use large-scale Bayesian inference, probabilistic programming, deep learning neural networks, and approaches that link them together. We are recruiting for 1-2 postdoc positions at the intersection of data science, machine learning, automated scientific discovery, and social science.  
 
Postdoctoral candidates will design and conduct independent research, in close collaboration with UChicago professor, James Evans, Director of Knowledge Lab, along with Josh Tenenbaum, computational cognitive scientist at MIT, and Michael Franklin, computer scientist and leader in systems design at the University of Chicago. Candidates must have substantial computational and data science background and hold a Ph.D. in Computer Science, Statistics, Applied Math, Physics, Sociology, Economics, Psychology or another Social/Behavioral Science. Candidates should have a strong publishing record. Regardless of degree, experience with social science theory and methods a strong plus. Comfort working collaboratively with a research team is essential.
 
Specifically, successful candidates will be responsible for generating and automatically decoding agent-models, and applying these techniques to real social systems. Experience with some of the following will be helpful: causal analysis, deep neural networks, Bayesian inference, probabilistic programming, machine learning, and machine understanding. Candidates will be involved in both innovating new methods for specific inference tasks, and assembling approaches into automated data analytic pipelines. The broader project will also involve crowdsourcing alternative approaches, so experience with crowdsourcing and intelligent model combination also a plus. Because we will be requesting social data from the agent-modeling teams, understanding social science data gathering methods and familiarity with agent-based and game theoretic models will be very helpful. A working knowledge of Python, as well as experience with relevant libraries (e.g., scikit-learn, pandas, tf, keras, pytorch, pymc, igraph, etc.) is required. Familiarity with bash, ssh, git, databases (e.g. mysql) and AWS is expected. Positions could begin anytime within the coming year, and as early as September 2018. Competitive salary & benefits.
 
To apply, please send CV and names for letters from at least two references to Candice Lewis, cllewis@uchicago.edu
 
Postdoctoral Position in the Science of Teams and Innovation
The Knowledge Lab at the University of Chicago seeks to hire an outstanding candidate for a postdoctoral research project with support from the National Science Foundation that uses large-scale data analysis and online team experiments to explore how to design teams for innovation and success. The project, titled “Understanding Team Success and Failure” was partially inspired by insight from our own recent work studying more than 50 million teams in science and technology that illustrated how smaller teams are much more likely than larger ones to produce work that disrupts the frontier. The project is also motivated by the realization that the vast majority of research on teams exhibits success bias, where data on failed teams remains under-recorded or censored. This project will involves a two-stage research program to understand how successful teams of different sizes and shapes “think differently” and can be designed to accelerate scientific, technological and creative discovery, invention and development. 
 
Postdoctoral candidates will design and conduct independent research, in collaboration with UChicago Professor and Knowledge Lab Director James Evans, and Dashun Wang, a network scientist and physicist from Northwestern University’s Kellogg School of Management. Candidates much have substantial computational and data science background and a Ph.D. in Sociology, Economics, Psychology or a related Social/Behavioral Science, Physics, Applied Math, Computer Science, Engineering or a related field, and a strong publishing background.
 
Specifically, the successful candidate will be responsible for assembling data, constructing features and evaluating success and failure outcomes for millions R&D teams over 100 years in terms of team size, network structure, role composition and experience. Second, insights developed from this investigation will enable the candidate, in collaboration with Evans, Wang and the broader team, to collaborate on the launch of large-scale online team experiments to isolate the causal mechanisms driving team success and failure. We will publish the results of analyses and experiments and make recommendations for policy to design teams optimized for specific purposes, such as advancing science and technology. Candidates must have experience with statistical models, inference, and knowledge of experimental design. Experience with Bayesian inference and machine learning a strong plus. Candidates should also have extensive experience (2 or more years) with scientific computing in Python. Positions could begin anytime within the coming year, and as early as September 2018. Competitive salary & benefits.
 
To apply, please send CV and names for letters from at least two references to Candice Lewis, cllewis@uchicago.edu