Open Postdoctoral position, faculty mentor Robb Willer

Important Info

Faculty Sponsor First name:

Robb

Faculty Sponsor Last Name:

Willer

Stanford Departments and Centers:

Institute for Human-Centered Artificial Intelligence (HAI)

Postdoc Appointment Term:

This is a full-time position based at Stanford University. The initial appointment runs for one year from the position start date. The position is intended to continue for a second year; any reappointment is contingent on satisfactory progress, program need, continued funding, and Stanford University approval. The appointment includes eligible medical, dental, vision, and life-insurance coverage through Stanford's benefits provisions for postdoctoral scholars. The final appointment and characterization of support will be issued through Stanford's Office of Postdoctoral Affairs.

Appointment Start Date:

Flexible depending on candidate degree conferral

Group or Departmental Website:

https://ai4pb.stanford.edu/

https://www.pascl.stanford.edu/

https://ai4pb.stanford.edu/

How to Submit Application Materials:

Please submit your application using this link.

Does this position pay above the required minimum?:

Yes. The expected base pay range for this position is listed in Pay Range field. The pay offered to the selected candidate will be determined based on factors including (but not limited to) the qualifications of the selected candidate, budget availability, and internal equity.

Pay Range:

$80,000 - $90,000

About the opportunity

Stanford University's Institute for Human-Centered Artificial Intelligence (HAI), in partnership with the AI for Public Benefit Lab (AI4PB), invites applications for a postdoctoral fellow under the mentorship of Professor Robb Willer. The fellow will be the inaugural dedicated postdoctoral scholar in AI4PB and will play a formative role in a growing interdisciplinary research program focused on using artificial intelligence to make a measurable difference in people's lives. The position also includes a secondary home in the Politics and Social Change Lab (PASCL).

The successful candidate will serve as a research co-leader on a flagship project developing, evaluating, and responsibly scaling a nonpartisan large language model (LLM) voter guide. The tool is designed to help citizens access accurate, comprehensible, and trustworthy election information while supporting, rather than replacing, human judgment. This work sits at the intersection of human-centered AI, computational social science, political behavior, persuasion, and democratic participation.

Research program

In a preregistered randomized controlled trial with 2,474 eligible voters in California and Texas during the week before the 2024 general election, participants who used the LLM voter guide rated it as accurate, trustworthy, and unbiased. They also reported stronger intentions to vote, greater alignment between their policy preferences and candidate choices, and lower animosity toward the opposing party (Mernyk et al., 2025). The project is conducted in partnership with Ballotpedia, a leading nonprofit provider of nonpartisan election information in the United States.

Building on the project's 2026 midterm deployment, the fellow will help analyze and publish findings, strengthen the technical and evaluative infrastructure, and design the next generation of studies for future U.S. elections. The broader aim is to understand when and how AI tools can improve access to civic information, support informed decision-making, and affect trust and democratic participation.

Key responsibilities

Research leadership. Co-design and co-lead the next phase of the LLM voter guide research program, from study conception through dissemination.
Technical development. Advance retrieval-augmented generation, source-grounding, and continuous source-updating workflows in collaboration with technical team members and external partners.
Evaluation. Develop rigorous approaches to assessing accuracy, source adherence, partisan neutrality, trustworthiness, and other dimensions of responsible AI performance.
Experimental research. Design and execute large-scale field and survey experiments across multiple states, including studies using verified outcomes where feasible.
Analysis and publication. Analyze complex data and lead manuscripts for peer-reviewed journals and conferences in human-centered AI and the social sciences.
Collaboration and translation. Work effectively with multidisciplinary researchers and civic partners, and communicate findings to academic, practitioner, and policy audiences.
Program development. Contribute to new research projects and proposals while pursuing complementary independent work at the intersection of AI, persuasion, civic information, and democratic participation.

Mentorship, community, and professional development

The fellow will receive close mentorship from Professor Willer spanning research design, publication strategy, project leadership, and preparation for the academic or applied job market. Mentorship will be tailored to the fellow's research and professional goals. The fellow will complete an Individual Development Plan during the first month and meet regularly with the faculty mentor to review research progress and career development.

The fellow will participate in HAI, AI4PB, and PASCL seminars, workshops, conferences, and lab training and will collaborate with PhD students, postdoctoral scholars, faculty, research staff, and external partners. Stanford collaborators include HAI, the Stanford Technology, Impact, and Policy Center, and the Institute for Research in the Social Sciences; external partners include Ballotpedia and Ipsos. Stanford computational infrastructure will support the work.

The fellow will also prepare brief quarterly fellowship reports describing research, writing, and presentation activities and will engage with relevant HAI programming throughout the appointment.

Review

Applications may be considered until the position is filled.
Questions may be directed to redekopp@stanford.edu

Required Qualifications:

PhD completed by the appointment start date in computer science, data science, human-computer interaction, psychology, sociology, political science, communication, economics, or a related field.
A record of rigorous, creative research and the ability to lead projects from design through analysis and publication.
Strong quantitative or computational research skills relevant to field experiments, survey experiments, causal inference, text analysis, machine learning, or related methods.
Excellent written and oral communication skills, including interest in communicating research to non-academic audiences.
Demonstrated ability to work collaboratively across disciplines and organizations while exercising intellectual and practical autonomy.
Strong organizational and interpersonal skills and the ability to prioritize effectively in a fast-moving research environment.

Preferred Qualifications:

Experience with large language models, natural language processing, transformer-based models, retrieval-augmented generation, or LLM application programming interfaces.
Proficiency in Python or comparable programming experience for implementing computational studies and working with large, complex datasets.
Experience evaluating political bias, fairness, neutrality, trustworthiness, safety, or source fidelity in AI systems.
Experience conducting large-scale online or field experiments, working with administrative or voter-file data, or collaborating on public-facing civic technology.
A strong publication record and demonstrated interest in the societal impacts of AI.

Required Application Materials:

Curriculum vitae.
A statement of interest of no more than 1.5 pages describing relevant research, methodological and technical experience, fit with the project, and professional goals.
Two or three published or unpublished research papers or other writing samples.
Names and contact information for three references. Letters may be requested separately during the review process.