We are seeking a full-time Pre-Doctoral Fellow to contribute to a research project developing an AI-driven global food security forecasting system. The project links high-resolution environmental data to subnational crop yields using machine learning, with the goal of leveraging these real-time data to predict unfolding food security issues. The fellow will be involved in data cleaning, model development, data dashboard construction, and academic and policy writing. The candidate will work under the supervision of Peter Huybers (Earth and Planetary Sciences) and Wolfram Schlenker (Harvard Kennedy School).
Fellows are expected to work full-time, in-person at Harvard in Cambridge, Massachusetts, and are encouraged to attend seminars and classes at Harvard while being a fellow. Fellows will receive competitive salary and benefits. International applicants are welcome to apply.
Applicants should hold or plan to receive an undergraduate degree by the summer of 2026.
Competitive candidates will have:
- A strong quantitative background in economics, math, statistics, and/or computer science
- Experience with programming, data science, and geospatial analysis (especially R, Stata, Julia, MATLAB, or Python)
- An enthusiasm for empirical research
- An ability to work independently and with attention to detail
- Effective communication skills and an ability to work in a collaborative interdisciplinary environment
The position is initially for one year, with the option for renewal.
- Cover letter
- Curriculum vitae
- Transcript of grades
- Paper (writing example) (optional)