Project Description
This project is part of a university-industry research program whose general objective is to increase scientific knowledge and develop tools to improve the management of drinking water distribution networks. The aim of this MSc or PhD project is to develop innovative predictive models for drinking water consumption. These models aim at regulating flows and pressures in distribution networks, managing proactively water needs, optimizing energy consumption of pumps and detecting major leaks. Climate change, population growth and urban sprawl are increasing pressure on water services and resources. Several approaches have been developed to secure and optimize drinking water supply. However, existing models for predicting water demand found in the literature have numerous limitations and shortcomings. To overcome these, this research program aim at developing innovative models and methodologies with practical spin-offs that can be used directly by the industrial partner, and also bringing economic benefits for Canadian municipalities.
The student will mainly use and develop machine learning models combined with statistical methods. The successful candidate will work as part of a diverse, multidisciplinary team and will have the opportunity to present the results of their work at international conferences and in high-level scientific journals.
Start date
January 2025
Research supervision
Fateh Chebana and Sophie Duchesne, INRS professors
Study program
MSc or PhD in Water Sciences (program details in French only, or custom-built program if necessary), Eau Terre Environnement Research Centre, INRS
Funding
All INRS students receive financial aid (more info).
Required qualifications
Preference will be given to students already in Canada.
Training
- For the MSc : Bachelor’s degree (or equivalent) in data science, statistics, civil engineering, water science, or other relevant discipline
- For the PhD : Research Master’s degree in data science, statistics, civil engineering, water science, or other relevant discipline
Other skills
- Experience in hydrology, modelling, and scientific writing are assets
- Knowledge of data science (statistics, machine and deep learning)
- Programming skills (R and/or Python)
- Interest in environmental applications and, in particular, water management
- Ability to learn new tools and work methods
How to apply
Please use the form below to send the following documents in PDF: (1) a letter of interests, (2) your CV, (3) a copy of BSc and MSc (if applicable) transcripts.