Sessional Lecturer - EES1119H
About The Position
This course provides an introduction to the field of ecological statistics. Students will become familiar with several methods of statistical analysis of categorical and multivariate environmental data. The course will provide a comprehensive presentation of the methods: analysis of variance, regression analysis, structural equation modeling, ordination (principal component & factor analysis) and classification (cluster & discriminant analysis) methods, and basic concepts of Bayesian analysis. Emphasis will be placed on how these methods can be used to identify significant cause-effect relationships, detect spatiotemporal trends, and assist environment management by elucidating ecological patterns (e.g., classification of aquatic ecosystems based on their trophic status, assessment of climate variability signature on ecological time series, landscape analysis). The course will consist of 3 hr-lectures/tutorials where the students will be introduced to the basic concepts of the statistical methods.
Requirements
- PhD degree in Environmental Statistics and/or Informatics
- Research experience and peer-reviewed publications in quantitative ecology
- advanced programming skills in R or Python
- significant experience with model-based management of biodiversity and species-at-risk problems
- Past teaching experience is a more relevant criterion than the need to acquire experience in respect of this posted position
- Availability for lectures scheduled on Fridays 1 PM - 4 PM
Responsibilities
- Preparation of lectures and labs
- lecturing
- creation of exams, term tests and assignments
- marking and marks management
- participation in the instructor/course evaluation process by the students
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What This Job Offers
Career Level
Mid Level
Education Level
Ph.D. or professional degree
