Computational Landscape Ecology - Cardille Lab at McGill University
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Invitation To Students

Prof. Jeffrey Cardille

McGill University 

Department of Natural Resource Sciences & McGill School of Environment

The Computational Landscape Ecology group of Prof. Jeffrey Cardille at McGill University is seeking MS and PhD students interested in computing-intensive landscape ecology applications. Students will join a dynamic and growing lab of graduate and undergraduate students working on a wide range of landscape ecology projects that demand a heavy component of computing power, including:

  • Forest connectivity estimation and validation at regional and continental scales, using Circuitscape 
  • Regional estimation of lake carbon content with remote sensing, GIS, and Bayesian estimation in 10**6 lakes in Quebec 
  • Spatial and statistical patterns of landscape metrics at continental and regional scales 
  • Flexible updating of land-cover classifications 


Successful candidates will have strong to moderate programming experience, ideally in R or Python or a similar language like Matlab or Java. Depending on the project, specific experience with certain tools will be of especially high value, including: remote sensing, GIS, simulation modeling, data mining techniques in very large data sets and coursework in statistics. Canadian students who would be eligible for federal and provincial scholarships are given high priority. 

Interested students should send the following:
  • a CV 
  • an unofficial grade transcript 
  • a letter explaining your interests 
  • a university writing sample of writing that you did during your undergraduate or graduate career
  • and the names of three references to: 
Professor Jeff Cardille, jeffrey.cardille@mcgill.ca 
Web site: cardillelab.weebly.com

Funding

In addition to funding through my research grants, I encourage students to secure their own funding either before or after joining my lab. Explore some potential sources of student self-funding below:

Click here to learn about a variety of funding sources.
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