We welcome people who are excited about longitudinal design, causal evaluation, education data science, AI, cost-effectiveness, and rigorous evaluation of education programs.
Postdoctoral opportunities
We have funding flexibility for postdoctoral scholars with strong training in statistics, econometrics, or related fields and research interests in causal inference, machine learning, and AI in education.
Potential projects connect methodological development with applied education questions using longitudinal, administrative, digital-learning, and experimental or quasi-experimental data.
If you are interested, please email Wei Li with your CV and a brief note describing your research interests.
Postdoctoral Fit
Strong candidates may bring expertise in one or more of the following areas:
- Longitudinal study design
- Monte Carlo simulation
- R or Stata software development
- Multilevel modeling
- Difference-in-differences
- Comparative interrupted time series
- Large administrative or digital-learning data
- Causal machine learning
- Learning analytics
Prospective Doctoral Students
Students interested in quantitative methods, longitudinal designs, causal evaluation, heterogeneous treatment effects, and education data science are encouraged to read about the Research and Evaluation Methodology program and reach out with a concise description of research interests.
Useful inquiry emails usually include:
- Your current program or academic background.
- The research projects or methods areas that interest you.
- Any experience with quantitative methods, programming, education data, or applied evaluation.
- A brief note on what you hope to learn through doctoral research.
Students should also review the University of Florida application timelines and the Research and Evaluation Methodology program requirements.
Collaborators
The lab welcomes collaborations with researchers, districts, digital learning platforms, funders, intervention developers, and education organizations seeking rigorous evidence about program impact, heterogeneous effects, longitudinal outcomes, implementation, and cost-effectiveness.
To stay connected with the lab, join the LCDS Lab mailing list for occasional updates on research, publications, training, events, and opportunities.
Example collaboration questions include:
- How many schools, classrooms, students, or repeated measures are needed for a longitudinal evaluation?
- Which students benefit most from a program, and under what conditions?
- How can digital learning or administrative data be used for interpretable, decision-relevant evidence?
- What does an intervention cost, and how do costs compare with estimated impacts?