The Princeton Water and Energy Technologies (WET) Lab, led by Prof. Z. Jason Ren, welcomes prospective PhD students interested in using AI and computational methods to address challenges in resource recovery and carbon management.
Our research connects data science with environmental and chemical engineering to understand complex systems, develop new technologies, and support better engineering decisions. We are looking for curious, motivated students who enjoy computational research and are interested in connecting methodological advances with practical applications.
Students may pursue research in areas such as:
- Machine learning and statistical modeling for environmental and chemical systems
- Large language models and AI agents for scientific and engineering applications
- Reinforcement learning, active learning, and Bayesian optimization
- Uncertainty quantification and interpretable, reliable computational methods
- Integration of computational approaches with experimental systems
Candidates should have strong programming skills and solid quantitative preparation. Experience in one or more of these areas is welcome. We value depth in a particular area, independent thinking, and enthusiasm for learning across disciplines.
We encourage applicants from environmental or chemical engineering, computer science, data science, applied mathematics, statistics, and related fields. Prior experience in water resource recovery or carbon management is helpful but not required.
Students will have opportunities to develop their own research directions with mentorship from Prof. Ren and the research team, contribute to interdisciplinary collaborations, and explore how research can translate into useful technologies.
How to apply
Please apply through the Princeton Civil and Environmental Engineering PhD program. The application deadline for Fall 2027 admission is December 21, 2026.
https://cee.princeton.edu/graduate/how-apply
Lab website: https://ren.princeton.edu/
Lab LinkedIn: https://www.linkedin.com/in/zjasonren/
Sample research articles from our group include:
https://www.nature.com/articles/s41893-026-01818-7