We are looking for a postdoctoral candidate to predict spatially the thickness of the organic layer (ECO) and physico-chemical properties in forest soils sensitive to peat accumulation.
The candidate will work with existing geo-referenced databases on soils and stand productivity, as well as with remote sensing products (e.g. aerial Lidar, Landsat) and forest inventory data.
He/she will join a dynamic team, will develop his/her own ideas in collaboration with the research team and will have the opportunity to use and compare different quantitative approaches (e.g. Machine Learning, Bayesian spatial models, etc.).
The project is a collaboration between the Institut de Recherche sur les Forêts (IRF- Université du Québec en Abitibi-Témiscamingue) and the Centre de Foresterie des Laurentides (CFL - Natural Resources Canada, Canadian Forest Service).
The position can be based on the candidate's choice, either in Rouyn-Noranda (UQAT) or in Quebec City (CFL-RNCAN). We are looking for a candidate with proven capabilities and previous experience(s) in spatial modelling and/or predictive statistics/mapping. Experience in the publication of scientific articles is an asset.