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Current artificial intelligence projects

Learn about our digital solutions projects and discover how we are using AI to modernize our work and improve our productivity. We are collaborating with Canada’s natural resource sectors to explore machine learning (ML) and to find the most efficient ways to integrate digital solutions.

Project title Timeline Project description Contacts Sector
Accelerated materials discovery using artificial intelligence, robotics and high performance computing 2019-2024
  • Accelerate the development of new materials for clean energy technology on self-driving laboratories using AI, robotics and high performance computing
  • Apply ML algorithms for decision making to recommend conditions for future experiments and random search

Mark Kozdras

  • Energy Technology
Open digital platform for forest value chain optimization Footnote * 2019-2023
  • identify new opportunities for increased competitiveness
  • locate the best clean growth investments
  • support equity and diversity in science
  • develop and improve the workforce

Mouloud Amazouz

Daniel Mazerolle

Eric Soucy

  • Energy Technology
  • Canadian Forest Service (CFS)
Intelligent decision support for industrial systems performance optimization 2016-2020
  • extract knowledge and identify patterns from industrial performance data
  • enhance operations across several industries
Mouloud Amazouz
  • Energy Technology
Groundwater information network 2009-Ongoing
  • distributed groundwater data network
  • uses AI to improve access to and integrate groundwater data from multiple sources
Boyan Brodaric
  • Lands and Minerals
Petroinformatics: a digital platform for optimizing oil refining 2019-Ongoing
  • create more efficient and cost-effective processes for oil refining operations
  • create digital platform to support decision-making for assessing oil refineries
Rafal Gieleciak
  • Strategic Petroleum Policy and Investment Office (CanmetENERGY Devon)
Mapping Canada's water and infrastructure 2017-Ongoing
  • accelerate the ability to create geospatial data layers for use in emergency management, flood mapping and change detection
  • enhance Canada’s foundational mapping layers
Karen Bronsard
  • Canada Centre for Mapping and Earth Observation (CCMEO)
ENERGY STAR brand integrity April 2018 (8 weeks experiment)
  • identify misuses of the ENERGY STAR brand on websites and social media
  • help to strengthen processes and refine policy to support consumers in decision making

Matt Naccarato

Erin Sullivan

  • Low Carbon Energy (Office of Energy Efficiency – OEE)
Lalor geochemical drillhole classification 2014-2018
  • provide support with the classification of geochemical data
  • predict 3-dimensional distribution of underground geological units

Ernst Schetselaar

Patrick Mercier-Langevin

  • Lands and Minerals
Tree-Ring Database Footnote * N/A
  • improve understanding of critical variations in tree growth
  • improve reliability of the Canadian Forest Service’s Tree-Ring Database
Martin Girardin
  • Canadian Forest Service
Robotic process automation for financial document management Footnote * 2020
  • automate linear tasks to reduce manual intervention
  • minimize error to increase accuracy and efficiency
Marc Cossette -
Optimizing electric grids and charging infrastructure for mass electric vehicles penetration Footnote * 2020
  • apply big data analytics and AI techniques to better understand and characterize EV infrastructure requirements
  • help electricity companies better manage electrical grids
Evgueniy Entchev
  • Energy Technology
Synthetic remote community energy load using machine learning techniques Footnote * Sept. 2020 – March 2021
  • better understand the load profiles of Canada's remote communities to enable the off-diesel transition
  • develop machine learning application to output synthetic electrical load profiles
Ryan Kilpatrick Energy Technology
Detecting deep mineral deposits with deep learning for resource extraction 2020-2022
  • develop machine learning/deep learning algorithms to help determine the probability of presence of valuable mineral deposits and rock type
  • can scale and replicate outcomes across Canada
Gilles Bellefleur Lands and Minerals
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