Ontario, Canada
Date Posted
31 Aug 2017

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PhD Project
University of Windsor

Applying Artificial Intelligence to Geospatial Systems for Quantification of Beneficial Use Impairments of River Systems (Computer Science)

Ontario, Canada
31 Aug 2017

NOTE: this position listing has expired and may no longer be relevant!

Position Description

The Great Lakes Institute for Environmental Research (GLIER) and the School of Computer Science, University of Windsor, is inviting applications for fully funded MSc. or Ph.D. studentships in the fields of Computer Science (Artificial Intelligence applied to Geospatial Systems) and, Biology and Ecology. The successful candidates will be working with a multidisciplinary research group whose work falls within the disciplines of machine learning, big data analysis, environmental science, ecology, and geospatial analyses. The successful candidate for Project 1 will be jointly supervised by Dr. Robin Gras (Canada Research Chair) and Ms. Alice Grgicak-Mannion and Project 2 by Dr. Ken Drouillard and Ms. Alice Grgicak-Mannion. Both projects are funded through Environment Canada’s Great Lakes Sustainability Fund with additional support provided through NSERC funds and University of Windsor’s Healthy Great Lakes Research Excellence Funds.

We are looking for talented and highly motivated individuals to analyze geospatial data and develop new probabilistic Beneficial Use Impairment (BUI) models for the purpose of assessing the health and potential “delisting” of the Detroit River and St. Clair River Areas of Concern (AOC). The main objectives for Project 1 will be to construct a standardized integrated geodatabase, using geographic information systems (GIS) and metadata templates (particularly ISO), to be the common framework for populating, storing, querying, sharing and viewing (in both a database and mapping context) disparate BUI related data across multiple AOCs. This application will allow users to identify commonalities and/or gaps in sampling schemes and data strategies, identify spatial and temporal trends/overlaps between BUI indicators and determine whether data can be synthesized with other BUI assessments for a more robust delisting quantification. Data will be extracted, collated into an integrated geodatabase and analysed for the purpose of sharing and generating synthesized models (i.e. Decision tree, artificial neural network, Bayesian networks, etc.) with other BUI assessments. The development of the geodatabase will mainly be under the responsibility of a software engineer working in collaboration with the candidate, but the conception and realisation of the analysis protocol will be under the responsibility of the candidate. Project 2 will focus on the BUI related data for sport fish consumption (SFC) (e.g. sediment contamination, indicator species, bioaccumulation models, mercury stable isotopes, etc.) to develop probabilistic, spatially explicit fish bioaccumulation models using steady state (food web scale) and non-steady state (indicator species scale) model frameworks.

For Project 1, candidates must have a strong back round in machine learning and artificial intelligence, with preference given to candidates that have published in top conference proceedings and journals in these fields. In addition, preference will be given to candidates that have previous experience working on environmental problems and using databases. Candidates must possess solid mathematical skills, in particular, probability and statistics. Strong theoretical knowledge of ANNs, Bayesian networks and other predictive models and exceptional programming skills (i.e. Python, Javascript, C++, etc…….) are a must, as well as, excellent knowledge of database systems and relational database programs (i.e. My SQL, PHP, ArcSDE, Oracle, etc). For Project 2, candidates should have experience with environmental fate and/or bioaccumulation theory, understanding of environmental chemistry and experience working with mathematical models.

All candidates will be expected to present his/her work in multiple ways (i.e. conference presentations, posters, proceedings, government reports, web applications and journal articles), therefore strong verbal and written skills in English are required. We also are looking for someone who can work well with others and share ideas on a regular basis.

For Project 1, candidates must have a Master’s degree in Computer Science
for the Ph.D. Position, however exceptionally strong candidates with a Bachelor’s degree will be considered as candidates for a Master’s degree with the expectation that the student be fast tracked to a Ph.D. after a year of study.

For Project 2, candidates must have a Master’s degree in Biology or Ecology for the Ph.D. Position, however exceptionally strong candidates with a Bachelor’s degree will be considered as candidates for a Master’s degree with the expectation that the student be fast tracked to a Ph.D.
after a year of study.

Successful candidates will receive an annual stipend of $19,000 CAD/year. Students will also be required to apply for other scholarship opportunities (NSERC, OGS, etc.) to increase their stipends.

How to Apply

Interested candidates should contact and submit a 1 page letter of interest, along with a CV to: Alice Grgicak-Mannion ( Dr. Robin Gras ( Dr. Ken Drouillard (

Position Category: Other. Position Type: PhD Project. Position Tags: data analysis, geospatial systems, and machine learning. Salary: €10'000 - €20'000.

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