PhD opportunity: Combinatorial Optimisation for Constrained Dynamic Conservation Planning
PhD opportunity: Combinatorial Optimisation for Constrained Dynamic Conservation
We are excited to offer a 3-year, fully funded PhD scholarship in Data Science and Artificial Intelligence at Monash University. This project focuses on combinatorial optimization for conservation planning, and will develop new methods to account for practical constraints and stakeholder requirements in biodiversity conservation, including how plans should adapt as conditions change over time.
This is an exciting opportunity to work with experts in combinatorial optimisation and decision science, and directly with conservation agencies to deliver impactful outcomes for nature and biodiversity. The candidate will develop cutting-edge skills in data science and AI that may include mixed-integer linear and non-linear programming, constraint programming, stochastic optimisation and reinforcement learning.
The scholarship is open to domestic and international applicants. We welcome applicants from quantitative disciplines (mathematics, computer science, data science, economics, AI) or from ecology and biology with a willingness to learn mathematical optimization.
Suggested reading
- Giakoumi et al. (2025). Advances in systematic conservation planning to meet global biodiversity goals. Trends in Ecology & Evolution, 40(4), 395–410.
- Chapman, M., et. al. (2025). Meeting European Union biodiversity targets under future land-use demands. Nature Ecology & Evolution, 9(5), 810–821.
- Silvestro, D., et al. (2022). Improving biodiversity protection through artificial intelligence. Nature Sustainability, 5(5), 415–424.
- Dujardin, Y., & Chadès, I. (2018). Solving multi-objective optimization problems in conservation with the reference point method. PLoS ONE, 13(1), e0190748.
Expressions of interest close 30 November 2026, for an early 2027 start. To apply, complete the EOI form and provide a one-page CV and academic transcripts.