2026–27 Faculty Research Projects

Applications for the 2026–27 predoctoral researchers program have been posted to Stanford Careers. Please do not contact PIs for more information. Application links for each position are active below, for each individual faculty project. You may apply now. Deadlines are indicated within each project description. 

Projects are grouped below by discipline. Click on a particular discipline of interest to jump to the relevant project(s):

Communication

Understanding Global Social Media Regulations

Jeff Hancock (Communication)

Project description

We are seeking a highly motivated pre-doctoral researcher to join a timely and impactful policy research project evaluating social media minimum age regulations. The project centers on real-world implementations and proposals in key jurisdictions, with a focus on Australia—which became the world's first country to enforce a mandatory minimum age of 16 for social media accounts in December 2025 —alongside a few other countries.

The research will involve organizing and analyzing early evaluation data. This is an outstanding opportunity for someone passionate about media, communication and psychology.

Key responsibilities

  • Conducting in-depth literature reviews and synthesize evidence on social media harms, minimum age policies, enforcement mechanisms, and evaluation methodologies across jurisdictions.
  • Organizing and analyzing early evaluation data.
  • Assisting with quantitative summarization and manuscript writing.
  • Helping maintain project resources (bibliographies, timelines, stakeholder contacts).

The position is full-time, under close supervision from the principal investigator. The PI commits to regular weekly mentorship meetings, detailed feedback, co-authorship opportunities on appropriate outputs, skill development in academic writing, and support for professional growth.

Required qualifications

Bachelor’s degree (completed or near completion) in a relevant field (communications, psychology, data science, or related disciplines).

Strong research and writing skills, with excellent attention to detail and ability to synthesize complex information.

Strong data analytic skills in quantitative methods.

Application

Application deadline for this project is May 1. Apply here to job 108664.

The Stanford Institute for Excellence in Survey Research: The Ball of Knowledge

Jon Krosnick (Communication/Political Science)

Project description

This is a full-time predoctoral research position focused on researching, compiling, and proposing recommendations for best practices in survey methodology. Our primary goal is to synthesize empirical literature to create the "Ball of Knowledge," a structured database and narrative handbook that distills findings from the field, identifies research gaps, and provides evidence-based recommendations for practitioners fielding surveys.

This position is supported by the Stanford Institute for Excellence in Survey Research (SIESR) and supervised by Prof. Jon Krosnick. Responsibilities span the full research pipeline: gathering literature from political science, communication, psychology, sociology, and related fields; systematic literature review and study coding; meta-analysis of empirical evidence; statistical analysis and synthesis; and manuscript preparation for academic and practitioner audiences. The RA will work closely with faculty and peer researchers on a collaborative, iterative basis to develop evidence-based recommendations grounded in rigorous empirical research.

Eligibility Requirements

The position is for recent graduates interested in pursuing a PhD in a social science, such as political science, communication, psychology, sociology, or other social science fields. The ideal candidates will have:

  • Strong quantitative preparation and coursework in survey research, political science, communication, psychology, or other social sciences;
  • Proficiency in statistical analysis of data using R or Stata
  • The ability to quickly and accurately read, synthesize, and critically evaluate primary empirical research publications
  • Excellent written communication skills for academic and general audiences
  • Attention to detail and meticulousness in current best practices of science fully documenting work

What you will learn

The position provides comprehensive training in tools and practices standard in academic and applied survey research:

  • Systematic literature review and evidence synthesis: Reading, abstracting, and coding primary empirical research; designing coding schemes; conducting meta-analyses.
  • Database development and management: Designing structured databases to organize empirical literature; implementing consistent coding workflows; quality control and cross-checking.
  • Quantitative analysis and statistical synthesis: Aggregating empirical findings using R or Stata; summarizing effect sizes and patterns across studies; producing publication-ready tables and figures.
  • Academic and practitioner writing: Drafting literature reviews, synthesizing complex empirical evidence, writing for diverse audiences (researchers, practitioners, journalists).
  • Collaborative research practices: Integrating citation management software; managing version-controlled manuscripts; incorporating evidence-based revisions.

Application

Application deadline for this project is May 1. Apply here to job 108826.

Economics

Technology, Provider Decision-Making, and Productivity in Healthcare

Rebekah Dix (Economics)

Project description

This is a full-time predoctoral research position in health economics, industrial organization, and innovation. The project uses detailed electronic health record data to study how technology — from clinical decision support systems to emerging AI tools — affects physician decision-making, and to develop new measures of provider productivity.

The work involves large-scale EHR data in cloud environments (Google Cloud Platform, BigQuery) and includes constructing measures of decision timing, risk management, and performance from high-frequency clinical records. Some of the work involves extracting structured information from clinical notes using natural language processing tools.

Required qualifications

The position is for recent graduates interested in pursuing a PhD in economics or a related field. Strong quantitative preparation and coursework in econometrics are expected. Experience with at least one programming language used in empirical research (e.g., Python, R, Stata, Julia, or MATLAB) is required. Familiarity with SQL, cloud computing, or text analysis is helpful but not required.

What you will learn

The position provides training in tools and practices standard in applied economics research:

  • Applied econometrics and research design with observational healthcare data
  • Large-scale data management in cloud computing environments (GCP, BigQuery, SQL)
  • Programming in Stata and Julia with structured, reproducible workflows — version-controlled code, build automation, peer-reviewed codebases
  • Git and GitHub for version control and project management
  • AI-assisted programming tools (LLM-based coding agents) to speed up day-to-day research work
  • NLP tools for extracting structured data from clinical text
  • Writing research deliverables — tables, figures, memos, draft paper sections
  • LaTeX for academic writing

The predoc will have weekly one-on-one meetings with the PI, weekly team meetings with coauthors, and regular communication on Slack in between. Attending department seminars and workshops is encouraged.

Application

Application deadline for this project is May 1. Apply here to job 108644.

Place-based Industrial Policy and Development

Tishara Garg (Economics)

This project studies how place-based industrial policy—such as government-promoted industrial parks and special economic zones—shapes economic development. In particular, it examines how these policies address market failures, including coordination failures in firms’ investment decisions, urban congestion, and environmental externalities.

The research combines multiple data sources—census and survey data, infrastructure and land-lease records, and satellite imagery—with quasi-experimental methods. A central focus is understanding how variation in land pricing, infrastructure rollout, and policy guarantees affects the timing, location, and coordination of firm entry and investment.

Preferred Qualifications

  • Strong programming skills (R and/or Julia preferred)
  • Training in econometrics, statistics, or a quantitative social science
  • Comfort working with data and willingness to engage with theory
  • Initiative, attention to detail, and genuine curiosity about economic questions

What You Will Learn

The position offers rigorous, hands-on training in quantitative policy research in economics. You will gain experience in:

  • Working with large-scale and high-dimensional datasets
  • Data construction, including web scraping
  • Spatial analysis (e.g., GIS/QGIS tools)
  • Writing structured, reproducible, and version-controlled code
  • Exposure to both empirical and, where relevant, structural and theoretical approaches
  • Writing and presenting research outputs
  • Close mentorship, including guidance on PhD applications

A central component of my mentorship philosophy is ensuring that the predoctoral researcher understands the broader intellectual motivation behind each task. You will be actively involved in discussions of research design, interpretation, and policy implications, with an emphasis on developing independent thinking and transferable research skills.

Application

Application deadline for this project is May 1. Apply here to job 108678.

Political Science

Migration, Sorting and Political Cleavages

Vicky Fouka (Political Science)

Project description

Since the mid-20th century, immigration has reshaped social identities and political conflict in Western democracies. During the same period, education became the central dividing line in politics. This project examines how immigration interacts with educational sorting to produce social experiences, identities, and political preferences that diverge across educational groups. The research combines population-level data (census, administrative, and commercial) and surveys from the United States and Europe over several decades to study how immigrants and natives sort geographically, how these patterns shape identity and political behavior, and how they contribute to long-run political realignment. The predoctoral researcher will assist with data collection, cleaning, and analysis, as well as background work such as literature reviews, qualitative case study analysis, and historical data collection. In addition to this core project, the predoc will also have the opportunity to work on related projects examining migration, identity, and political economy in historical contexts (19th and early 20th centuries) in the US and Europe.

Eligibility Requirements

The ideal candidate will be proficient in STATA and R, have a background in applied statistics/econometrics and causal inference, and be familiar with Python and agentic AI tools. The most important requirement is a willingness to learn more about these methods and the substantive questions they are used to answer.

What you will learn

  • Experience in handling and analyzing large administrative and survey datasets
  • Familiarity with methods used to conduct empirical research in applied social science
  • Exposure to scholarship on migration, political economy, and group identity
  • Exposure to the growing field of historical political economy (HPE)
  • Active involvement in all stages of the research process

Application

Application deadline for this project is May 1. Apply here to job 108663.

AI Governance

Rob Reich (Political Science)

Project description

This project investigates the governance of artificial intelligence through the integrated lenses of political theory, social science, and computer science, examining how democratic societies can shape AI systems that increasingly shape our political, economic, and personal lives. The first workstream, AI and democracy, explores how algorithmic systems affect democratic institutions, deliberation, and participation — probing questions of how AI concentrates or disperses political power, shapes information environments, and poses risks and opportunities for self-governance. The second workstream, law, policy, and regulation, analyzes the modes of authoritative rule-making that states and intergovernmental bodies are deploying to govern AI — from global, federal, and state lawmaking. The third workstream, professional norms, standards, and responsibilities within AI and ML development, examines the various forms of self-governance, including how engineers, researchers, and firms develop codes of conduct, safety benchmarks, and accountability frameworks, and how these quasi-regulatory norms interact with, substitute for, or undermine formal governmental oversight. Several projects underway explore the emergence of an ecosystem of independent evaluators. Taken together, these workstreams develop an integrated theory of AI governance that takes seriously both the novelty of the technical systems at stake and the enduring political questions of authority, accountability, and legitimacy.

Eligibility Requirements

Excellent analytical and organizational skills and outstanding academic credentials.

The pre-doctoral fellow will work on (1) drafting reports, conducting literature reviews, and leading the preparation of research memos, (2) organizing and convening research workshops/symposia with faculty, postdoctoral fellows, and graduate students, (3) leading and organizing public events at which research is discussed and presented, (4) supporting current and future teaching and lecture activities, including a new course on AI governance.

Application

Application deadline for this project is May 1. Apply here to job 108675.

Electoral Change in the US, 2016–2026

Douglas Rivers (Political Science)

Project description

This project uses big data from a large-scale Internet panel and voter files to understand changes in public opinion and voting behavior over the past decade. Although parties have alternated in power almost continuously over this period, voting patterns have been remarkably stable with control of government determined by small shifts in preferences and turnout. Traditional data collection, using relatively small samples and cross-sectional designs, are ineffective under these conditions. The predoctoral scholar will have the opportunity to work with unique and challenging data sources to address fundamental questions in American politics.

Eligibility requirements

  • Interest in U.S. elections and voting behavior
  • Experience in designing and analyzing survey data
  • Programming with R and Python and the use of modern software engineering tools
  • Statistical modelling and graphics

What you will learn

  • Handling large-scale datasets and LLMs
  • The practice of effective statistical graphics

Application

Application deadline for this project is May 1. Apply here to job 108666.

AI-Native Research in Chinese Politics and Quantitative Methods

Yiqing Xu (Political Science)

Project description

We invite applications for a full-time predoctoral researcher to join an AI-native research lab at IRiSS. The researcher may specialize in either quantitative methods or Chinese politics. The lab develops empirical research and methodological work in causal inference and panel data, supported by a large set of curated datasets and AI-driven research infrastructure.

The position is designed for candidates planning to pursue a PhD in political science, economics, statistics, computer science, or a related field.

On the quantitative methods track, the fellow will contribute to AI workflow development and evaluation, support large-scale replication and reproducibility projects, and improve research efficiency through scalable, automated pipelines.

On the Chinese politics track, the fellow will contribute to publishable research on contemporary political and economic topics, drawing on extensive existing datasets and new data collection. Chinese reading proficiency is required.

Eligibility requirements

Applicants should have strong quantitative training and programming skills in R or Python. Familiarity with version control and reproducible research practices (e.g., through GitHub) is a must. A strong background in mathematics, statistics, or computer science is preferred. Candidates must demonstrate advanced ability to use modern AI tools for coding, data analysis, and research workflows. Applicants are strongly encouraged to provide evidence of this ability, such as a writing sample, GitHub repositories, project portfolios, or demos. Important: except for the writing sample, all other materials should be provided as public links in the applicant's CV or personal statement.

What you will learn

During the year, the predoc will gain experience in publishable empirical research, AI-integrated workflows, research software development, and methodological work in causal inference and panel data. The position emphasizes building scalable, AI-assisted research pipelines that enhance efficiency and reproducibility.

Application

Application deadline for this project is May 1. Apply here to Job 108647.

Psychology

Leveraging AI to Support Evidence-Based Treatments for PTSD

Johannes Eichstaedt (Psychology)

Project description

Millions of Americans with PTSD lack access to evidence-based psychotherapy. AI—especially large language models—could help close this gap, but careful development and evaluation are needed. This project advances two tools: TherapyTrainer, which uses LLMs to simulate patient interactions for training therapists in written exposure therapy, and an AI-enhanced CPT Coach app that gives veterans real-time feedback on therapy homework. The IRiSS predoctoral researcher will test and refine these tools, coordinate clinical trials, and analyze data across multidisciplinary teams in the CREATE Center for Advancing Therapy with AI, the Computational Psychology and Well-Being (CWPB) Lab, and the Fidelity, Adaptation, Sustainability, and Training (FAST) Lab. The position suits candidates interested in mental health research, AI, natural language processing, or user experience who plan to pursue a PhD in clinical psychology.

Eligibility Requirements

The ideal candidate will have a background in psychology, computer science, or human-computer interaction, exposure or interest in clinical research methods, psychopathology, or PTSD and trauma, and experience programming with R and Python. Previous experiences in research participant recruitment, clinical trial coordination, product management, user experience testing, and developing AI-psychology products will make a candidate especially well-suited for this role.

What you will learn

  • Gaining exposure to the process of developing LLM-based tools for clinical psychology and real-world use of AI for mental health
  • Working with interdisciplinary teams of clinical psychologists, computational psychologists, data scientists, and computer scientists.
  • Exposure to randomized clinical trials for PTSD
  • Contributing to conference presentations and journal manuscripts
  • Preparation for graduate school in clinical psychology, computer science, or a related field

Application

Application deadline for this project is May 1. Apply here to job 108752.

Nightingale: Advancing the Science of Data and Visualization Literacy

Judith Fan (Psychology)

Project description

Data visualizations are everywhere—in science, medicine, journalism, and policy—yet surprisingly little is known about how people actually learn to read them, or what makes some visualizations harder to interpret than others. The Nightingale project, led by Prof. Judith Fan (Department of Psychology, Stanford University), addresses these questions at the intersection of cognitive science, the learning sciences, and data science. We aim to develop rigorous measures of visualization literacy, build theories of how people reason with quantitative information, and study how data literacy skills are acquired in authentic educational contexts.

We are seeking a motivated predoctoral researcher to join the team for 2026–27. The predoc will contribute to empirical studies, data analysis, and scientific writing, and will receive close mentorship from Prof. Fan and a collaborative team of graduate students, postdocs, and faculty across Stanford, UCLA, and Northeastern.

Eligibility requirements

  • Bachelor's degree (or expected by summer 2026) in psychology, cognitive science, statistics, computer science, or a related field
  • Strong quantitative skills and programming experience in R and/or Python
  • Enthusiasm for empirical research and scientific writing
Preferred qualifications
  • Experience with behavioral experimentation or online participant recruitment
  • Background in psychometrics, computational modeling, or statistics education

What you will learn

  • Experimental design and large-scale behavioral data collection
  • Psychometric and statistical modeling
  • Scientific writing and publishing
  • Preparation for graduate school in psychology, cognitive science, or a related field

Application

Application deadline for this project is May 1. Apply here to job 108649.

The Learning Variability Network Exchange (LEVANTE)

Michael Frank (Psychology)

Project description

LEVANTE is a global research network to improve our understanding of variability in learning and development through coordinated data collection. The predoc will work on one of two teams at Stanford. The first is the partnerships team that creates creating stimuli and materials for LEVANTE assessments and works with international partners to adapt these to different languages and contexts. The second is the development team that implements tasks and data pipelines to help our international partners collect data.

Eligibility requirements

The LEVANTE team is a supportive environment in which to get more experience with research and contribute to a large and meaningful project about children's variability across contexts. Strong applicants will have some background in psychology, cognitive science, human development, or a related field as well as an interest in working with children. For candidates interested working on the development team, experience programming in javascript is required.

Application

Application deadline for this project is May 1. Apply here to job 108650.

The BabyView Project

Michael Frank (Psychology)

Project description

The BabyView Project is a research initiative dedicated to capturing children’s everyday experiences via high-resolution videos taken from the infant perspective, and to using the resulting video data to better understand cognitive development. Our dataset is currently the largest open video dataset of children’s everyday experience to date, both in terms of the number of hours and the diversity of the participating families; data collection is currently ongoing. Predocs will be part of the BabyView team, supporting families by onboarding them to the project, curating incoming data, building and distributing cameras, and participating in data analysis.

Eligibility requirements

The predoc will work closely with a group of other research coordinators who join into a collaborative group that works on the operations of the project as well as analysis of the data. An ideal candidate will have a background in cognitive science or psychology, strong organizational skills, and an interest in working with children and families. For candidates interested in participating in data analysis, strong quantitative skills (e.g., statistical analysis in R) will be important. Candidates will have opportunities to participate in generating research ideas using the dataset to answer novel questions about child development.

Application

Application deadline for this project is May 1. Apply here to job 108652.

Causality in Cognition

Tobias Gerstenberg (Psychology)

Project abstract

The Causality in Cognition Lab (CiCL) studies how people learn to represent the causal structure of the world, and how they use this knowledge to predict what will happen, explain what happened, and hold others responsible for the consequences of their actions.

When a detective comes to a crime scene, how do they figure out what happened and who did it? When we watch a sports game, how do we determine which player should receive the most credit (or blame)? When a road accident happens, how do we judge whose fault it was? These are just some of the questions that motivate our research. We believe that giving satisfactory answers to these questions requires studying how people build mental models of the world—models that incorporate people's intuitive physical and social understanding, and that support simulating how things could have turned out differently. Recently, we have also become more interested in the question of how such counterfactual simulations support learning and decision-making.

Preferred qualifications

  • Strong programming skills (ideally both Python and R)
  • Quantitative data analysis skills
  • Prior research experience related to cognitive science
  • Ideally, experience with programming and running experiments

What you will learn

The predoc will help with developing computational models, designing and running online experiments, analyzing and visualizing data, and preparing conference presentations and journal manuscripts.

Application

Application deadline for this project is May 1. Apply here to job 108653.

The Effects of Early Life Stress on Neurodevelopment and Psychopathology Across Adolescence

Ian Gotlib (Psychology)

This position has been filled.

Project Description

In our lab we are examining the neural and developmental mechanisms that underlie the effects of early stress on depression and anxiety across adolescence and into young adulthood. Predocs familiar with analyzing neural data will find this position particularly valuable. The predoc will help with participant scheduling, data analysis, scanning, and clinical interviewing. The predoc will also help with preparing IRB renewals and modifications.

Required Qualifications

Predocs should have a Bachelor’s degree in a relevant academic area (psychology, neuroscience, cognitive science, biology, etc.), strong interpersonal skills, an ability to think critically, troubleshoot, and work independently. Experience with R, MATLAB, and Python will be helpful, as will experience scanning.

What You Will Learn

The predoc will have the opportunity to gain valuable research skills and interact with adolescents and young adults in both clinical and non-clinical populations, and to explore professional interests within clinical psychology, affective neuroscience, and developmental psychopathology. This position will provide the predoc with relevant experience and opportunities for presenting posters at conferences and publishing papers.

Application

This position has been filled.

Visual Cortex as a Window to Neurodevelopment

Kalanit Grill-Spector (Psychology)

Project abstract

This project examines how the visual cortex develops throughout childhood as children learns new skills like reading and navigate new social environments like changing schools. We will be conducting longitudinal behavioral and neuroimaging measurements of brain structure and function using MRI as children transition through schooling environments in 4-6 year olds (preschool -> school), 9-12 year olds (elementary -> middle school), and 13-16 (middle school -> high school).

Eligibility requirements

  • Undergraduate degree in psychology, cognition, or neuroscience
  • Quantitative skills including coding proficiency or R
  • Strong organizational skills
  • An interest in working with children and families and being part of a large research project.

What you will learn

The predoc will learn about the visual system, developmental psychology, fMRI, and neuroscience. They will work with a research team including graduate students and postdoctoral fellows. The predoc will be trained to conduct and analyze behavioral and MRI experiments, present the results for scientific audiences both in verbal and written form, design experiments and develop hypotheses.

Application

Application deadline for this project is May 1. Apply here to job 108654

Sociology

Building a Homelessness Policy Engine

David Grusky (Sociology)

Project description

What would it take to make homelessness policy a full-on success story? The conventional answer to this question, an answer that’s partly correct, is that we need more research on the payoffs to competing homelessness programs. This standard-playbook call is absolutely the right call but it’s not enough by itself. We must also establish a straightforward process for converting available evidence into evidence-based policy. If we don’t strengthen this conversion process, we run the risk that people will come to believe that evidence is useless because policy isn’t based on it anyway. The twofold purpose of our project is (a) to build a "policy engine" that assists local policymakers in converting evidence into policy that's consistent with local conditions and objectives, and (b) to use our access to key administrative data to fill the gaps in evidence on homelessness that must first be filled before a credible policy engine can be built.

The predoc will be tasked with programming and statistical analysis of linked administrative data on homelessness (drawing on the full playbook of quasi-experimental approaches to program evaluation). The goal is to address the key remaining gaps in evidence on homelessness that must be filled to make evidence-based policy viable at the local level.

Application

Application deadline for this project is April 10. Apply here to Job 108633.

Back to Top