Apply Now for New Postdoctoral Scholar Positions at the Knowledge Lab at the University of Chicago

How to Apply

Apply Here for all Postdoctoral Positions

Attachments to include:

  1. Cover letter, describing your interest in and qualifications for pursuing interdisciplinary research;
  2. Curriculum vitae (including publications list);
  3. Two published papers or equivalent writing samples that best demonstrate your expertise and fit for the position;
  4. Research statement (2-4 pages) that outlines your research achievements and agenda, as well as your service and outreach activities (optional);
  5. Contact information for three or more scholars who know your work and are willing to write letters of reference;
  6. An example (e.g., GitHub links or code in any language) of working software you have written (optional);
  7. Link to a professional webpage and Google Scholar page (optional)

University Info

All University departments and institutes are charged with building a faculty from a diversity of backgrounds and with diverse viewpoints; with cultivating an inclusive community that values freedom of expression; and with welcoming and supporting all their members. We seek a diverse pool of applicants who wish to join an academic community that places the highest value on rigorous inquiry and encourages diverse perspectives, experiences, groups of individuals, and ideas to inform and stimulate intellectual challenge, engagement, and exchange. The University’s Statements on Diversity are at https://provost.uchicago.edu/statements-diversity. 

 

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, gender identity, national or ethnic origin, age, status as an individual with a disability, military or veteran status, genetic information, or other protected classes under the law. For additional information please see the University’s Notice of Nondiscrimination. Job seekers in need of a reasonable accommodation to complete the application process should call 773-834- 3988 or email equalopportunity@uchicago.edu with their request.”

Postdoctoral Scholar Positions on AI, Science & Technology Prediction

We seek outstanding candidates for government and philanthropically funded postdoctoral positions developing large language models (LLMs) and associated AI systems to (1) observe the global system of science and technology at scale; (2) predict future science and technology; and (3) experimentally explore alternative futures and how to configure the world to achieve them. Postdoctoral Scholars will work at the interface of AI research on transformers and novel deep learning designs and realized large-scale systems in collaboration with the Allen Institute for Artificial Intelligence (e.g., its AI-powered Semantic Scholar and Open Language Model, OLMo) and the U.S. Department of Energy’s Trillion Parameter Consortium. This work will be in collaboration with computational social scientists (e.g., economists, sociologists, physicists) furthering the science of science and innovation in order to guide U.S. and global strategic investments in science and technology. Candidates will work in the Knowledge Lab in the Division of Social Sciences at the University of Chicago, directed by Prof. James Evans. Technological change drives economic change, creating new jobs while making others more efficient, replacing industries in cycles of creative destruction that generate new goods and services to promote human productivity and prosperity. Understanding technological progress and leadership is key to ensuring sustainable prosperity, security, and equity, and buffering against economic shocks. Nevertheless, despite enormous data on science and technology, little is understood about what pathways could ensure advance in critical technology capacity, production, and use. This effort will require improved situational awareness regarding the global techno-scientific environment, strengthened capacity to predict technological advance across that environment, and establishing causal linkages between specific policy levers for technological progress and leadership. We will model and predict the global system of technological development to understand and shape technological capabilities. To enable holistic exploration, we will build a powerful large language model (LLM) atop precise data on global technology funding, research, intellectual property, products, and their mentions, linked by the people, regions, and organizations producing and consuming them, to dynamically account for complex and emergent interdependencies among diverse technological domains. We seek to construct our model to function as a global observatory of the innovation system, and as a virtual laboratory to simulate alternative outcomes (e.g., advances predicted to result from specific funding, educational, or organizational policies). Our LLMs will also support the estimation of structural models to causally identify the impact of funding on interlocking discoveries, overlapping supply chains, global networks of skilled labor, and industrial organizations that drive or impede international technology leadership.

The initial term of these positions is one year, with possibilities for renewal. The positions require annual renewal for up to 4 years and involve very competitive salary and benefits packages. We review applications on a rolling basis. Target start dates are flexible. Candidates must have a proven record of publications and completed projects that demonstrate an outstanding ability to work with data. Compensation includes a competitive salary and benefits plan and assistance with relocation to Chicago.

Qualifications:

  • Ph.D. in computational social science (e.g., sociology, management, economics, political science), complex systems (e.g., physics, ecology), network science, computer science, applied mathematics, statistics, or related fields. 
  • Prior experience in programming and working with large-scale data; expertise in machine learning is a strong plus. 
  • Active publication record and participation in the scientific community;
  • Strong communication skills;
  • Ability to work in a highly collaborative and interdisciplinary environment.