2025–26 Faculty Research Projects
The application cycle for the 2025–26 Predoctoral Researchers Program will open by the end of January 2025.
Projects are grouped below by discipline. Click on a particular discipline of interest to jump to the relevant project(s):
Political Science
Immigration Policy Lab
Jens Hainmueller (Political Science)
Project description
The Immigration Policy Lab (IPL) is seeking a predoctoral researcher to assist with research on migration using administrative datasets from governments, international organizations, and other sources. IPL has a robust research program analyzing the impact of different policies and programs on migration patterns and dynamics and the well-being of refugees, immigrants, and host communities. The predoctoral researcher will analyze existing administrative data to which IPL already has access, and find and integrate new administrative data sources on migration and other economic and social outcomes from different countries. The researcher will work with IPL Faculty Directors Jens Hainmueller and David Laitin, as well as a team of researchers, data scientists, and staff. They will attend lab and project team meetings and work in an independent and collaborative environment to produce policy-relevant research.
Eligibility requirements
Candidates should have strong coding proficiency in R. Additional proficiency in Python would be preferred but not required. Candidates should have experience with statistics and data analysis. No specific degree is required, but candidates should have a strong technical background commonly found in economics, statistics, applied mathematics, computer science, or related fields. Knowledge of, or experience with, migration data and government administrative datasets is a plus, but not required.
2024 U.S. Presidential Election Survey
Douglas Rivers (Political Science)
Project description
During the 2024 election, we collected a large and unusually detailed panel survey of US voters. The purpose of this project is to determine why voters gave Trump more support in 2024 than they had in the two previous presidential elections; who switched or was mobilized; what their motivations were; and whether this represents a short or long-run shift.
Eligibility requirements
- Interest and coursework in public opinion and voting
- Prior programming experience
What you will learn
- Handling large-scale datasets and LLMs
- The practice of effective statistical graphics
Psychology
AI-Driven Approaches to PTSD
Johannes Eichstaedt (Psychology)
Project description
Millions of Americans experience mental disorders, including PTSD, and many lack access to treatment, especially evidence-based psychotherapies proven effective at treating these disorders. Artificial intelligence, especially large language models (LLMs), holds potential to close the science-practice gap in mental health care, yet careful development and evaluation is needed to ensure this is done safely. This project investigates two AI-driven applications aimed at enhancing the reach, fidelity, and effectiveness of PTSD treatments: TherapyTrainer, which uses LLMs to simulate patient interactions for training therapists in an evidence-based treatment for PTSD, and an AI-enhanced Cognitive Processing Therapy (CPT) Coach app that provides veterans with real-time feedback on therapy homework. The IRiSS predoctoral researcher will contribute to both initiatives by testing and refining tools, coordinating clinical trials, and analyzing data. Working within multidisciplinary teams at the Computational Psychology and Well-Being Lab and the Fidelity, Adaptation, Sustainability, and Training Lab, the predoc will gain hands-on experience in clinical psychological research, app development, and responsible research practices. This position is particularly well suited for candidates with interests or experience in clinical psychology, AI, natural language processing, or user experience.
Eligibility requirements
- Desired majors include psychology, computer science, human-computer interaction.
- Desired coursework includes courses on research methods, psychopathology, or PTSD and trauma.
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
- Data management
- Data visualization
- Recruitment and coordination of research participants
- User experience testing
- Prompt engineering for clinical psychology
The Learning Variability Network Exchange (LEVANTE)
Michael Frank (Psychology)
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 a team at Stanford that creates and implements measures for LEVANTE, helping with collecting and curating data with young children and with creating stimuli and materials for assessments.
Eligibility requirements
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. Some programming experience is desirable but not required.
What you will learn
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.
Sociology
The American Voices Project
David Grusky (Sociology)
Project description
The Center on Poverty and Inequality (CPI) seeks a predoc to join our Voices Lab and contribute to our work on the American Voices Project (AVP), an innovative new research infrastructure for collecting and analyzing mixed-methods data across diverse research topics. The AVP is the country's first large-sample, nationally representative, mixed methods data platform that makes it possible to discover what people are thinking, feeling, doing, and experiencing. The core of the AVP's innovative methodology is long-form immersive interviews with individual respondents that promote trust and authentic sharing about one's family, work, friends, politics, religion, and everyday experiences. These conversations are then followed up with more conventional survey questions and linked to administrative data (with consent). The resulting database of thousands of narrative transcripts and survey responses, accessed through a secure server, provides a powerful tool for conducting research across diverse topics using a variety of methods: qualitative, quantitative, mixed methods, and AI-based. (For examples of research based on AVP data, see this recent special issue of the Russell Sage Foundation Journal, Part 1 and Part 2.) The predoc will play an important role in our work to launch the AVP's exciting next phase, as we implement new Large Language Model (LLM) tools to build and enhance the AVP data and also open up access to the AVP platform to an expanded cohort of external researchers.
The predoc's primary responsibilities will include synthesizing the coding of AVP transcripts and survey data, conducting disclosure avoidance review for research papers produced using AVP data (i.e., ensuring that publicly disclosed data follows requirements for protecting confidentiality of AVP respondents), supporting external researchers who are using the AVP platform, and assisting with LLM implementation. The predoc will also have opportunities to contribute to academic publications and presentations stemming from this project. Depending on the predoc's individual interests and skills, they may have opportunities to contribute to other areas of CPI's research as well.
With respect to mentorship and learning opportunities, CPI Faculty Director Prof. David Grusky and senior CPI research staff will work closely with and mentor the predoc through bi-weekly or more frequent individual meetings, weekly team meetings, and frequent communication in person and via email. The predoc will also be fully included in CPI's regular research team programming, including participating in and having opportunities to present at weekly CPI lab meetings, joining the weekly CPI coffee break and periodic research fellow lunches, attending the weekly doctoral Inequality Workshop, and interacting regularly with doctoral students, postdocs, undergraduate research assistants, research staff, CPI faculty affiliates, and visiting researchers from around the world.
Eligibility requirements
Ideal candidates will have strong organizational and problem-solving skills and attention to detail, prior experience with coding and analysis using R, Stata, and/or Python, comfort with learning new software and technical skills, and interest in the type of research that can be conducted using the AVP platform.
Building a Next-Generation Measure of Poverty for California
David Grusky (Sociology)
Project description
The Center on Poverty and Inequality (CPI) seeks a predoc to join our California Lab and contribute to our work to understand and measure poverty and inequality—and assess policies and programs that aim to address these challenges—within California. With nearly 40 million residents, California is home to almost 1 in 8 Americans. The state has an ethnic, racial, and immigrant makeup that is unusually diverse, spread across economically and demographically diverse regions. Although California is an economic powerhouse, it consistently ranks as extremely high in poverty, inequality, and homelessness among U.S. states. At the same time, state and local policymakers have committed to building, piloting, and scaling a range of innovative policies and programs that aim to reduce poverty and inequality. As a result, the state has become a key national laboratory for testing new solutions to pressing problems. CPI's California Lab seeks to inform and assess these important efforts.
The predoc will play a key role in advancing our work to develop a next-generation California-specific measure of poverty that uses linked administrative data to measure poverty at a high resolution, enabling research to identify gaps in the social safety net and to test the effects of policy on poverty. The predoc's primary responsibilities will include coding and analyzing linked individual-level survey and administrative data that are accessed through Stanford's on-site secure Federal Statistical Research Data Center (FSRDC). The predoc will also contribute to CPI's work to produce the California Poverty Measure (CPM), a poverty measure constructed in public-use Census survey data (in collaboration with Public Policy Institute of California), with responsibilities to include coding and analyzing American Community Survey public-use microdata and contributing to policy briefs and data visualizations based on CPM data, including analyses and updates for CPI's related California Data Dashboard. The predoc will also have opportunities to contribute to academic publications and presentations stemming from these projects. Depending on the predoc's individual interests and skills, they may have opportunities to contribute to other areas of CPI's research as well.
With respect to mentorship and learning opportunities, CPI Faculty Director Prof. David Grusky and senior CPI research staff will work closely with and mentor the predoc through bi-weekly or more frequent individual meetings, weekly team and/or partner meetings, and frequent communication in person and via email. The predoc will also be fully included in CPI's regular research team programming, including participating in and having opportunities to present at weekly CPI lab meetings, joining the weekly CPI coffee break and periodic research fellow lunches, attending the weekly doctoral Inequality Workshop, and interacting regularly with doctoral students, postdocs, undergraduate research assistants, research staff, CPI faculty affiliates, and visiting researchers from around the world.
Eligibility requirements
Ideal candidates will have strong organizational and problem-solving skills and attention to detail, prior experience and strong skills in coding and analysis using Stata and/or R, comfort with learning new data and technical skills, and interest in policy-oriented research. Note that in order to work with confidential data accessed through the FSRDC, the selected predoc will need to complete a comprehensive Census Bureau federal background check upon hiring.