The Regulation, Evaluation, and Governance Lab (RegLab) at Stanford University is hiring full-time pre-doctoral Research Fellows to join our research team. This is a minimum one-year position, with the option of renewal.
This position is a great next step for those considering graduate school, law school, and/or business school in the future. Prior Research Fellows have been accepted by PhD programs in computer science, economics, and political science and JD programs at top schools (e.g., Harvard, Stanford, Princeton, Yale). In recent years, fellows have been coauthors on RegLab publications for PNAS, JAMA Health Forum, Nature Sustainability, the American Economic Journal: Economic Policy, the American Law and Economics Review, the Journal of Law, Economics, and Organization, ACM FAccT, the Journal of Empirical Legal Studies, and the Stanford Law Review.
About Us: Stanford RegLab is an impact lab that partners with government and nonprofits to use machine learning and data science to modernize the public sector. We are an interdisciplinary team of data scientists, social scientists, engineers, and lawyers who are passionate about building high-impact demonstration projects for the future of governance. Some of our partners include the Environmental Protection Agency (EPA), the Internal Revenue Service (IRS), the Department of Labor (DOL), and various public interest organizations.
As a member of our research team, you will:
- Work closely with the Faculty Director, Research Director/Manager, data scientists, and teams of fellows and students to drive forward a diverse research program focused on machine learning and policy evaluation
- Conceptualize suitable empirical methodologies and models
- Collect, manage, and structure quantitative datasets
- Conduct statistical analyses of complex datasets and interpretation of results
- Write reports and prepare manuscripts
- Design and implement state-of-the-art machine learning models, algorithms, and statistical models, while leading the collection of new data and the refinement of existing data sources.
- Have the opportunity to receive co-authorship on research papers
Qualifications:
- A Bachelor's or Master’s degree in a relevant quantitative field (e.g., computer science, data science, statistics, engineering, mathematics, economics, or a related field)
- Outstanding academic credentials and intellectual creativity
- Eagerness to take initiative and solve intricate problems
- Excellent time-management skills and ability to work effectively with minimal supervision
- Exceptional research, analytical writing, and communication skills
- Programming experience in R, Python, Stata, SAS, and/or other languages
- Prior research experience and coursework in the empirical social sciences is preferred, but not required
- Self-guided, self-learner, and engaged in the mission of the Lab
- Experience working with machine learning frameworks (TensorFlow, TF, PyTorch, Scikit Learn, etc.), NLP, computer vision, or related fields is a plus
Application Instructions:
To be considered, please submit the following items along with your online application:
- CV
- Transcript(s) (unofficial is fine; please include all full-time programs)
- Project/code samples or Github
There will be two rounds of application review. The deadline for the first round is 7AM PDT December 2, 2024. The deadline for the second round is 7AM PST Jan 6, 2025. Applications will be evaluated on a rolling basis and preference will be given to first-round applicants.
