This position is part of the Post-Doctoral Fellows Association and has an initial appointment of three years.
This position has a comprehensive benefits package.
Location - This role is in-person at North Campus, Edmonton.
Position Summary
The Forest Growth & Yield Lab is seeking a highly motivated, mathematically-minded Postdoctoral Scholar to lead the development of next-generation forest dynamics models for the Western Boreal and Rocky Mountain regions.
The core goal is to produce models that can be applied in operational forest management and timber supply analysis while integrating advances in remote sensing. The work will critically evaluate existing Canadian and international growth models and enhance the representation of silviculture, climate, succession, and genetic effects in predictions of forest dynamics and development. The successful candidate will help translate these models into forms that support sustainable forest management decision-making.
We are looking for a quantitatively strong researcher who has (or is strongly motivated to develop) interest and aptitude in both forest science / stand dynamics / forest management and remote sensing / geospatial data integration. We prioritize demonstrated independence, intellectual curiosity, and the ability to identify what you don’t know and learn it quickly.
The Faculty of Agricultural, Life & Environmental Sciences is a leader in teaching, research, and community engagement across diverse fields including food systems, sustainable resource management, human ecology, and environmental sociology. With more than 500 faculty and staff, over 1,500 undergraduates, and 600 graduate students, the Faculty of Agricultural, Life & Environmental Sciences leverages 25,000 acres of research land across Alberta to deliver high-impact academic programs and applied research in partnership with industry and communities.
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Lead the critical evaluation, augmentation, and redevelopment of growth and yield / forest dynamics models, including testing alternative model architectures to better incorporate climate, succession, and silviculture effects.
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Design and implement robust, reproducible workflows that integrate long-term permanent sample plot (PSP) data with remote sensing observations and other geospatial datasets.
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Exercise high-level independent judgment to advance the project from broad conceptualization through to validated, simulation-ready model systems suitable for management applications.
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Lead the preparation of technical reports, high-impact peer-reviewed publications, and knowledge-transfer activities with government and industry partners.
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Collaborate effectively with lab members and external partners while maintaining strong ownership of the modelling work.
We recognize that few candidates will arrive with deep expertise in every technical area. We are looking for researchers who are excited by the integration of quantitative forest modelling and remote sensing and who have the independence and curiosity to strengthen their knowledge in both domains as the project evolves. We value demonstrated quantitative rigour and a genuine interest in producing models that can be used in real forest management contexts.
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PhD awarded within the last 5 years in Forestry, Forest Science, Quantitative Silviculture, Forest Biometrics, or a closely related field with a clear focus on growth and yield, stand dynamics, or quantitative forest modelling.
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Demonstrated ability to build, validate, troubleshoot, and apply complex statistical or mathematical models to real forestry problems (examples: non-linear mixed-effects models, nonparametric models, process-based or hybrid models).
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Strong evidence of independent research leadership — the ability to take a broad goal, break it into actionable technical steps, and drive progress with limited day-to-day supervision.
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Proficiency in R and/or Python with a commitment to clean, well-documented, and reproducible code and workflows.
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Interest in (or foundational understanding of) forest stand dynamics, silviculture, or forest inventory, and interest in (or experience with) integrating remote sensing or geospatial data into ecological or forestry models.
Preferred Qualifications:
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Experience integrating remote sensing data (e.g., lidar or spectral, multiple-platform) data into forestry or ecological models.
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Familiarity with Canadian forest management contexts, timber supply analysis, yield curve development, or similar applied modelling frameworks.
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Experience working with large datasets or database tools (SQL or equivalent).
Application Instructions
Click "Apply Now" to submit your cover letter and resume.
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