The Stanford Social Media Lab works on understanding psychological and interpersonal processes in AI-mediated communication. The predoctoral research fellow will work with Professor Jeff Hancock, researchers, and graduate students in the lab to conduct experiments and computational field experiments to understand the dynamics of AI-mediated communication. The position is ideal for individuals with an interest in pursuing a Ph.D. in psychology or communication seeking to gain greater familiarity and experience with research before applying to graduate schools. Please contact Sunny Liu at sunnyxliu [at] stanford.edu (sunnyxliu[at]stanford[dot]edu) if you have questions. Job responsibilities will include:
conducting data collection;
performing statistical analyses; and
designing and running experiments.
Familiarity with R and computational skills are highly desired.
The US House of Representatives has shifted its internal rules over the past decades to give more control to the parties over the lawmaking process. This project will document the House and party rules over time in order to assess how they are associated with polarization of the parties, legislative productivity, and public trust in Congress. Additionally, the project will consider developments in congressional campaign finance practices and how they relate to legislative behavior.
The predoc will contribute to the development of databases, conduct literature reviews, and contribute to the data analysis. Conditional on sufficient contribution to the project, there is the possibility of co-authorship.
Some statistical/data analysis coursework or training; experience with STATA or R preferred, and a willingness to learn these statistical packages is expected; facility with Excel for database management; comfort summarizing academic articles in political science.
What you will learn
The position will enable growth in data analysis, experience with putting together scholarly articles, and a capacity to evaluate social science research.
Project C: Long-run Dynamics of Change in Ideas and Identities
How do ideologies, group identities, and societal values and beliefs change during periods of economic and political transformations? This research agenda studies these changes in Europe and the US from early modernity to the 20th century, taking advantage of new data and techniques to quantify social change. The predoctoral fellow will work on two distinct, but interrelated projects. The first one relies on large text corpora (newspapers, legal, and religious texts) for tracing changes in identity and ideology that followed the Industrial Revolution in Europe, but also changes that preceded and enabled industrialization and modernization. The second project uses data from census and birth registries in Europe and the US to track identity changes of immigrant communities in their new environments, or of religious minority communities in response to economic and political change. The predoc will help with cleaning, merging, and visualizing data from different sources, and analyzing data using NLP and applied statistics methods. Some background work, such as conducting literature reviews or collecting data from historical sources, may also be required.
The ideal candidate will be proficient in STATA and/or R, have a background in applied statistics/econometrics and causal inference and be familiar with Python and text-as-data methods. The most important requirement is 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 historical datasets
Familiarity with methods used to conduct empirical research at the intersection of history and social science
Exposure to scholarship on political economy, group identity and social dynamics
Exposure to the growing field of historical political economy (HPE)
Active involvement in all the steps of the research process
The Immigration Policy Lab (IPL) is seeking a predoctoral fellow to assist with research associated with its GeoMatch tool, a matching algorithm that helps refugees, asylum seekers, and other immigrants find the locations where they are most likely to thrive. Within the GeoMatch portfolio, specific areas of research include testing the tool's effectiveness through randomized control trials, measuring how users interact with the tool, and exploring ways to improve the tool’s effectiveness through algorithmic and methodological innovations.The predoctoral fellow would work with IPL Faculty Directors Jens Hainmueller, David Laitin, and Jeremy Weinstein, and an active product team at IPL focused on deploying the GeoMatch tool and supporting its associated research. They would attend team meetings with the staff at IPL and the faculty team that directs the project. Example projects that the predoctoral fellow would assist with include improving the machine learning models used by GeoMatch by finding and integrating new data sources, creating usability and explainability documentation for the tool, designing and analyzing surveys on immigration location preferences, and analyzing refugee out-migration patterns within administrative datasets.
Candidates should have strong coding proficiency in R. Additional proficiency in Python would be preferred but not required. Candidates should have experience with machine learning and statistics. 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.
This project aims to develop a causal inference toolkit for panel data, specifically designed for social science applications. Relevant examples are available on Professor Xu's website. The predoc will contribute to software development, support methodological advancements in causal inference, and assist in producing tutorials and a textbook on the subject. Additionally, the predoc will have opportunities to engage in applied research on the political economy of China. Regular weekly updates to the supervising professor are mandatory.
Solid foundation in mathematics, including linear algebra and probability theory
Completion of intermediate-level courses in econometrics or statistics
Ability to comprehend original research in applied statistics or econometrics
Proficiency in programming (e.g., R, Python, or C)
There has been a sudden and dramatic shift in age distribution in the global population. Whereas a century ago, only 5 % of the population in the United States was over 65, today, there are roughly the same number of five-year-olds and 65-year-olds. Age diversity is a novel resource, yet age segregation limits potentially productive exchanges and relationships. Several projects in my laboratory explore intergenerational relationships within families, workplaces, and neighborhoods. A full-time predoc can be integrated into all of these projects.
The Life-span Development Lab within the Department of Psychology at Stanford University is seeking a highly motivated and organized individual to join our team as a full-time Lab Manager. As the Lab Manager, you will play a crucial role in supporting ongoing research projects and ensuring the smooth operation of the lab. Responsibilities include managing day-to-day activities, overseeing participant recruitment, coordinating research protocols, and maintaining a well-organized and efficient work environment.
The ideal candidate will have a strong background in psychology, excellent organizational and communication skills, and the ability to work collaboratively with a diverse team of researchers. Previous experience in a research setting, familiarity with psychology, and proficiency in basic statistics and relevant software are highly desirable.
What you will learn
Basic familiarity with statistics, data entry, and data analysis
Project G: How the Infant Brain Processes Food Rewards
Predocs are invited to apply for a new project in the Scaffolding of Cognition team focused on how the infant brain supports reinforcement learning and cognitive control. Reinforcement learning is a type of learning that orients behavior towards maximizing reward (e.g., physical comfort, food). Cognitive control is the exertion of mental effort to facilitate goal-directed behavior and inhibit undesirable behavior. Infants are capable of both reinforcement learning and cognitive control, yet how their brains support these capacities remains unknown. In this project, we wish to study how the infant brain responds to the most potent reward they regularly experience: food. Additionally, we will ask how infants can inhibit a response when there is no reward. We will conduct functional magnetic resonance imaging while infants are awake and watching a video screen. They will be given a pacifier that can supply milk. We will then time the delivery of milk to events on the screen they are watching so that they can learn to associate the delivery of milk with the stimuli. This will allow us to measure how the brain processes rewards and whether it predicts upcoming rewards. In other conditions, sucking will not lead to reward, and thus, they are expected to learn to withhold sucking in these contexts.
The position is ideal for individuals who wish to gain experience that will prepare them for a Ph.D. in Cognitive Neuroscience. Candidates with undergraduate degrees outside of Psychology and Neuroscience who nonetheless have the appropriate skills will be considered closely.
The predoc's responsibilities will be to construct the food delivery pump, pilot its use, and conduct experiments testing the neural response to food rewards. Developing the pump will require substantial technical and practical skill; however, I will closely guide them throughout this process.
As part of the technical skills, the candidate is expected to be familiar with programming (e.g., MATLAB, Python, high-performance computing), as evidenced by class work or independent research. Additional desirable qualities include statistical rigor, neuroimaging experience, and familiarity with office tools (e.g., MS Office, Slack). Finally, candidates must show initiative, problem-solving skills, and excellent communication.
What you will learn
The successful candidate is unlikely to have all of the skills listed above; however, it is expected that by the end of their time, they will have advanced skills in each of these domains.
Project H: Measuring, Modeling, and Improving Data Visualization Literacy
Scientists use data visualizations to make new discoveries and communicate their findings to the public. So it is important for everyone to be able to understand data visualizations (and not just professional scientists!). The catch is that researchers are still figuring out how a person’s brain changes as they learn these skills and how to measure these changes reliably. Our project will contribute to this effort by developing improved measures to test different theories of data visualization literacy, with the longer-term goal of improving how core data literacy skills are taught. If you join us, you can expect to be closely involved in designing & conducting the behavioral studies to test existing & new measures of data visualization literacy, as well as coordinate with our partner organizations.
The Cognitive Tools Lab generally looks for prospective lab members who have a positive attitude, strong motivation, scientific curiosity, and willingness to quickly/independently learn new things. For more information, please visit our lab website.
What you will learn
Data analysis and statistical reasoning
Web programming for developing behavioral experiments
Maintaining reproducible research workflows
Professional communication and organizational skills
The Causality in Cognition Lab (CiCL) studies how the mind learns to represent the causal structure of the world, and how we use this knowledge to predict what will happen, explain what happened, and hold others responsible for the consequences of their actions. In our research, we formalize people’s mental models as computational models that yield quantitative predictions about a wide range of situations. To test these predictions, we use a combination of large-scale online experiments, interactive experiments in the lab, and eye-tracking experiments. The predoc will help with developing computational models, designing and running online and eye-tracking experiments, analyzing and visualizing data, and preparing conference presentations and manuscripts. Find out more about what we do, what we value, and how to join us.
Project J: Investigating Cognitive Development during Early Childhood Using an Online, Scalable, Meta-Science Platform
This position has been filled. New applications are no longer being considered.
The Social Learning Lab in the Department of Psychology at Stanford University is accepting applications for the IRiSS Predoctoral Research Fellowship. The selected predoc will assist with empirical research investigating the cognitive underpinnings of social cognition and communication. Research in the Social Learning Lab asks: How do humans communicate with others by reasoning about their own and others’ mental states, and how does this ability develop in early childhood? To answer these questions we conduct in-person research with adults and children, both in-person (in lab, at partner museums, and at local nursery schools) and online (e.g., Prolific, Zoom, Lookit). This position will offer a great opportunity for prospective PhD students in Cognitive Science and Cognitive Development (Psychology) to gain the expertise and experience critical for a successful application, and sharpen their research interests.
In the Social Learning Lab, the predoc will have the following responsibilities:
assisting with data-collection for in-person or live-on-Zoom developmental experiments, which requires being comfortable interacting with families and conducting developmental studies with preschool-aged children;
aiding in stimuli design, qualitative data coding, quantitative data analysis as well as other aspects of the empirical research process;
depending on the fellow’s expertise and interest, there may be opportunities to engage in developing experiments for adults in VR environments.
general quantitative skills for data analysis (e.g., multilevel linear regression);
experience interacting with children and families;
prior research experience in cognitive science & cognitive development;
organizational and communicative skills;
the ability to work independently and in teams.
We also expect the successful candidate to show initiative to learn (both from others and by self-teaching) and demonstrate a clear motivation for engaging in cognitive development research.
What you will learn
This predoctoral position will have the opportunity to work on two research programs. The primary program seeks to evaluate how the representations and motivations of one of our partner organizations, Black Girls Code, are perceived by different key constituents, including current and prospective clients from their target client demographic and potential funders. We plan to accomplish this in at least two ways. First, we will build on Dr. Ellen Markman's findings that the linguistic formulations often used to indicate equivalence between two groups can actually backfire. Specifically, subject-complement statements such as "girls are as good at math as boys," lead people to believe that boys are actually naturally more skilled in math than are girls. For Black Girls Code, this raises the possibility that hearing the name of the organization may tacitly prompt them to believe that White boys are the standard when it comes to coding. We will assess whether this perception occurs, the impact of this possible perception, and strategies to address it among client and partner populations. Second, we will build on Dr. Jordan Starck's findings that the reasons organizations provide for why they commit to issues regarding diversity, equity, and inclusion, can have diverging impacts on Black and White Americans' perceptions of the organization. While instrumental reasons based on the benefits embracing diversity can provide (e.g., maximizing human capital, boosting employee retention, developing creative team outputs) are particularly appealing among White Americans and make them feel like they will gain greater value, feel greater belonging, and experience greater identity safety there, the opposite tends to be true among Black Americans. For Black Americans, moral reasons focused on the values and principles undergirding diversity commitments are associated with these better outcomes. As such, organizations striving to appeal to diverse stakeholders are in a conundrum in determining how to effectively communicate about the motivations driving their commitments. We will explore the implications of, and possible strategies to address, this conundrum in the case of Black Girls Code.
Depending on the predoctoral scholar's interests and skillset, they may have the opportunity to pursue an additional line of work related to racial inequality in media, education, or the law.
Strong applicants will have had some prior experience conducting social science research, including designing and implementing experiments and observational studies. They will also have some experience with quantitative and qualitative analyses. Familiarity with psychological research regarding race, prejudice, intergroup relations, and inequality is required. They will also have some proficiency in SPSS or R for statistical analyses.
What you will learn
Predoctoral scholars can expect to gain substantial experience in designing and conducting experiments and observational studies, quantitative and qualitative analyses, literature reviews, and managing research partnerships with professional organizations. They may also gain experience with scientific writing.
This position has been filled. New applications are no longer being considered.
This project will examine how gentrification and declining housing affordability affect residential instability in the city of Oakland, CA. The project is in partnership with the City of Oakland's Department of Housing and Community Development. The project involves analyzing patterns of residential displacement, financial instability, and housing conditions and assessing a homelessness prevention pilot program through large-scale consumer data, program applications, surveys, interviews, and focus groups. The predoc’s primary responsibilities will be managing and analyzing datasets, producing deliverables for policymakers and broader public audiences, and coordinating data collection. Additionally, the predoc will work closely with project partners to help apply the findings of the research towards the evaluation and improvement of housing policy interventions. Tasks related to data management include documentation, data merges, measures, coding, and analysis, and the predoc will direct undergraduate and graduate students working with these data. The predoc will also prepare data visualizations for academic and non-academic deliverables and write reports and other translation pieces to communicate results to partners, practitioners, and broader public audiences. The predoc will also have opportunities to contribute to academic deliverables stemming from this project. Prof. Hwang will work closely with and mentor the predoc through weekly individual meetings, weekly team meetings with all undergraduate and graduate students working on the project, partner meetings, and frequent communication via slack/email.
Ideal candidates will have strong communication and organizational skills, prior background on social science research design, and experience with qualitative coding (e.g., NVivo) and statistical software (e.g., R).