Funded opportunities
Ph.D. - Environmental drivers of network structure
This project follows from recent research in the lab on the prediction of species interactions in space, on probabilistic ecological networks, and on the scaling of network structure.
The goal of this project is to understand how the coupled responses of species to their environmental predictors can drive the spatial distribution of network structures. This project will use data from different sources, including predation, infection, and mutualism data, and rely on a broad variety of methodological approaches, including applied machine learning, species distribution modeling, phylogenetic analysis, and macro-ecological simulations.
Specifically, the project will contribute to exploring the following questions:
- Are interacting species showing more similar responses to environmental predictors than expected by chance?
- Is this similarity protecting species pairs from interaction disruption under climate change?
- Is the similarity of responses to the environment driven more by phylogeny or by interaction status?
In addition to these ecological questions, a core expected methodological development is to demonstrate how we can propagate the effect of a single predictor variable to the co-occurrence of a pair (or group) of species, using the framework of Shapley Additive Explanations.
The selected candidate will receive advanced training in data science, machine learning, high-performance computing, software engineering, and technical writing; these are high-demand transferable skills even outside academic careers.
This project is fully funded (annual scholarship: 30k CAD), and will benefit from the new funding scheme at the Département de Sciences Biologiques, Université de Montréal, which includes guaranteed teaching assistant time in addition to financial incitatives when submitting articles (for approx. 4.5k CAD a year). The selected candidate will be supported in applying to external (NSERC, FRQNT) and internal grants and fellowships.
Required skills and qualifications: M.Sc. in ecology or related field; fundamental training in quantitative research (biostatistics, data science); basic training in programming (any language).
Desired skills: evidence of publication output (pre-prints encouraged); good programming skills.
Application process: review of applications will start immediately and continue until the position is filled; to apply, send a two-pages (maximum!) CV, and a one-page cover letter, to timothee.poisot@umontreal.ca.
Stages de recherche au premier cycle
TBA