Canada: PhD positions in hybrid SciML and numerical weather prediction (SFU)

We invite applications for several funded MSc and PhD positions in the Applied and Computational Mathematics program at Simon Fraser University (SFU), to work on hybrid scientific machine learning and numerical weather prediction (NWP), in collaboration with Environment and Climate Change Canada, with a particular focus on the Canadian Arctic.

This project aims to fuse traditional numerical methods with machine learning techniques such as operator learning and active learning to enhance predictive capabilities in regions where traditional weather prediction performs poorly. These positions are part of a larger, multi-institutional initiative on hybrid ML–NWP across Canada. Accurate forecasts in the Canadian Arctic are critical for the safety, food security, and resilience of Inuit Nunangat communities.
 
Applicants should have an undergraduate degree in mathematics or a closely related discipline. Experience in numerical analysis is expected; familiarity with (or a strong interest in learning) machine learning is highly desirable.
 
Applicants should apply to SFU’s Applied and Computational Mathematics graduate program through the following link, listing Professors Ben Adcock and Steven Ruuth as potential supervisors:
https://www.sfu.ca/math/graduate/prospective/admissions.html
 
Applicants should include a short statement in their application describing their interest in this project and discussing relevant experience or expertise.  Successful candidates will receive a competitive funding package (typically a combination of teaching assistantships and research assistantships) for the normal duration of their program. The deadline for applications is Jan 13, 2026.  

For inquiries, please contact Ben Adcock (ben_adcock@sfu.ca) or Steven Ruuth (sruuth@sfu.ca).