Current Openings

Postdoctoral or Predoctoral Position for NSF “Critical Technologies” network:

We seek outstanding candidates for an NSF and AFOSR-funded post-doctoral positions in computational social science at the University of Chicago. Working under the supervision of James Evans in Knowledge Lab, post-docs and pre-docs will explore ways of understanding and forecasting the evolution of global science and technology in ways that facilitate actionable recommendations for strategic funding and support.

Positions will be associated with Knowledge Lab ( and the Social Science Division ( at the University of Chicago ( Positions are funded for 2-3 years, contingent on annual reappointment and continued funding.

Our aim is to apply and develop computational methods, including deep learning (e.g., deep auto-encoders, generative transformers, deep reinforcement learning), network models, and other bespoke and/or ensembled machine learning models to data on science, invention, and downstream products and businesses to the broad challenge of understanding the distribution and direction of global scientific and technological advance. From these data-driven models, we seek to understand and make grounded recommendations regarding important scientific and technologies in which national innovation systems may usefully sponsor and invest.

Applicants are expected to have strong qualifications in machine learning and/or computational social scientific methods, models and inference. Successful applicants will have the opportunity to work with other post-docs, researchers and students in the social and computational sciences (e.g., machine learning, computational linguistics, applied math).

For the postdoc position, minimum qualifications for this position are a PhD or expected PhD in social or cognitive science (e.g., sociology, economics, political science, psychology); statistics, information science, computer science; or the natural sciences (e.g., physics/ecology) with a demonstrated commitment to computational methods and social science or complex systems topics. Predocs should consider the position based on similar criteria and interests and must possess a relevant Masters or Bachelors degree. Women and members of underrepresented groups are strongly encouraged to apply.

Interested candidates must submit their information to this form:

1) cover letter, describing your interest in and qualifications for pursuing interdisciplinary research;

2) curriculum vitae (including publications list);

3) contact information for 2-3 scholars who know your work and are willing to write letters of reference or engage in a phone call; 4) papers or projects (including software) that reflect your best work.

Positions can begin immediately, and are strongly desired for this 2022-2024 period. Compensation includes a competitive salary and benefits plan and assistance with relocation to Chicago.

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 James Evans,

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 James Evans,