Fully Funded PhD in Modeling Boreal Forest Fire Regime and Ecosystem Resilience (Earth Science, Environmental Science, Computer Science) University of Calgary in Canada

The University of Calgary is offering a fully funded PhD position focused on modeling boreal forest fire regimes and ecosystem resilience under climate change in eastern Canada. This opportunity is ideal for candidates interested in environmental science, earth science, computer simulation, and quantitative data analysis. The research will center on the development and application of a computer-based vegetation model to study vegetation–fire–insect interactions across the boreal forests, with a domain covering regions from the Atlantic Ocean to the Canadian Prairies. The project is funded by Natural Resources Canada’s Wildfire Resilient Futures Initiative and led by the Ontario Forest Research Institute, part of the Ontario Ministry of Natural Resources.

Key research tasks include model development, data assembly and management, experimental design, and analysis and visualization of model outputs. The position is primarily desk-based but may involve field visits to project sites in Ontario. Applicants should have strong programming skills (R, Python, C++, Fortran), quantitative and data analysis abilities, and fluency in English. Preferred candidates will have a Master of Science in a STEM field or significant research experience, coursework in ecology, climate science, global change biology, geography, and related fields, and skills in scientific computer programming and GIS.

The position requires full-time, in-person presence at the University of Calgary main campus in Alberta, Canada, with possible secondments to project partners elsewhere in Canada. The earliest start date is January 1, 2026, subject to arrangement with the selected candidate. Funding includes a minimum annual stipend of CAD $34,500, with additional support for travel, conferences, and research expenses, for a period of 3 years.

To apply, candidates should submit a single PDF file containing a one-page statement of research interests and experience, a short CV, scans of university transcripts, and English language test results (if applicable) via the provided application link. Assessment will consider collaborative skills, drive, independence, and the ability to contribute to ongoing research within the department.