Canada: Postdoc: Hybrid SciML–NWP for Canadian Arctic Weather Forecasts

We invite applications for a Postdoctoral Fellowship in the Department of Mathematics 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 (ECCC), with a 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.  This position is 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 hold a PhD (by the start date) in mathematics, applied mathematics, computer science, atmospheric science, physics, or a closely related discipline, with a strong research record in SciML, numerical PDEs, NWP, or related areas, and strong scientific computing skills (e.g., Python and modern ML frameworks). SFU will nominate the selected candidate for a Canada Impact+ Research Training Award (Postdoctoral), subject to eligibility and internal selection. These awards are valued at $70,000 per year for two years; nominees must be currently studying or working abroad and cannot have a current affiliation with a Canadian institution.

The appointment is for up to two years (12 months, renewable for a second year), with a flexible start date of July 1, 2026 or September 1, 2026. Applicants should apply via MathJobs at: https://www.mathjobs.org/jobs/list/27974.

Applications received by January 31, 2026 will receive full consideration. For inquiries, please contact Ben Adcock (ben_adcock@sfu.ca) or Steven Ruuth (steven_ruuth@sfu.ca).