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Postdoctoral Scholar, Data & Democracy Research Initiative

The University of Chicago is seeking Postdoctoral Scholars focused on the intersection of democracy and data science. The postdoctoral scholar will be part of the new Data & Democracy research initiative, a major interdisciplinary collaboration jointly led by the University of Chicago Data Science Institute (DSI) and Center for Effective Government (CEG).

The Data & Democracy research initiative is a unique collaboration between computer scientists, statisticians and political scientists to better understand democracy in the digital age. This initiative will investigate critical questions concerning the impact of misinformation on effective government, how online communication translates into offline political behavior, and the implications of the consolidation of online media platforms for free speech. We invite researchers to join this initiative to spearhead new interdisciplinary research projects as part of a growing community of scholars.

Application review will begin March 21, 2022. However, the application will remain open until filled.


Data Science Institute Postdoctoral Scholars Program

The UChicago Data Science Institute invites applications for Postdoctoral Scholars with appointments starting on or after July 1, 2022. We are interested in individuals who wish to deepen their knowledge of cutting-edge data science and computing research while developing additional expertise in a specific, applied problem domain.

This unique program provides postdocs with the opportunity to pursue original research on significant questions in data science, while also developing specialized domain expertise in one or more complementary areas such as behavioral science, healthcare, and public policy.

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Postdoctoral Scholar in AI Uncertainty Quantification and Automation of Scientific Instrumentation

The Deep Skies Lab ( at Fermilab is excited to announce two postdoctoral researcher positions in artificial intelligence for science with Dr. Brian Nord and his research group! We focus on the development and application of statistical learning and AI algorithms.

Statisticians, computer scientists, AI experts/practitioners, physicists, and astronomers are invited to apply. In these positions, you will have the opportunity to build and apply new algorithms for 1) uncertainty quantification in AI and 2) for automated design and operation of scientific instruments. Our group also welcomes new research ideas and directions. You will also have ample opportunity to collaborate in AI- and cosmology-related research collaborations. We publish in peer-reviewed journals and conferences, and there will be opportunities for travel and inter-institutional collaboration.

Broadly, the activities in our group support the innovation, development, and application of advanced statistical and computing techniques to accelerate scientific discovery. In particular, each postdoc position focuses on a distinct task. One position is aimed at the exploration and development of AI uncertainty quantification methods for science analyses. The other position centers on automating the design and operation of scientific instrumentation (e.g., self-driving telescopes).


Postdoctoral Scholar in Cancer-Focused Machine Learning

The University of Chicago Pritzker School of Molecular Engineering is seeking candidates for 2–3 postdoctoral scholar research positions, funded primarily by the UChicago Data Science Institute. Postdoctoral scholars will lead interdisciplinary research projects and collaborations in the areas of machine learning, genomics, clinical cancer care, and image-based computer vision. Advances in genomics have led to new cancer therapies that target specific genetic or molecular features, raising the potential for effective personalized treatments with reduced side effects. However, the majority of patients treated with targeted therapies do not respond as predicted, and detailed patient genomic information is expensive to acquire. The goals of this initiative are to develop new artificial intelligence approaches that improve targeting of cancer treatment by combining multiple streams of genetic information with tumor pathology images. Postdoctoral scholars will create new methods that draw upon computer vision and machine learning to extract essential contextual information about individual cancers from tumor samples, utilizing genomic, transcriptional, and image-based features.


Postdoctoral Scholars & Graduate Students, Deep Learning in Cosmology and Astrophysics

We are seeking collaborators for a short-term project on the development and application of statistical and deep learning techniques for the analysis of Cosmic Microwave Background (CMB) imaging data. We are seeking postdocs and graduate students with experience in statistics and deep learning applications for computer vision in the context of astronomy and cosmology. The scope of work includes the innovation, development, and application of deep learning techniques for the analysis of CMB. Both projects will likely touch on issues and opportunities to advance deep learning algorithms, methods in uncertainty quantification for machine learning, and high-performance computing. We are seeking to collaborate as soon as possible, and aim to complete work (including submit publications) by late 2022. We can provide funding for graduate students or a postdoc for about a year.

For more information, contact Brian Nord.

Postdoctoral Scholar, Mapping & Mitigating the Urban Digital Divide

The University of Chicago is seeking a Postdoctoral Scholar focused on internet performance and measurement to work closely with Nick Feamster, Neubauer Professor of Computer Science and Faculty Director of Research at the UChicago Data Science Institute. The Urban Digital Divide project is a major interdisciplinary research initiative, funded by, focused on addressing the issue of Internet access by using large-scale Internet measurement and data science to help communities and stakeholders to better assess the quality and accessibility of broadband Internet access.


The University of Chicago is an Affirmative Action/Equal Opportunity/Disabled/Veterans Employer and does not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national or ethnic origin, age, status as an individual with a disability, protected veteran status, genetic information, or other protected classes under the law. For additional information please see the University’s Notice of Nondiscrimination.