Grants and Contributions
About this information
In June 2016, as part of the Open Government Action Plan, the Treasury Board of Canada Secretariat (TBS) committed to increasing the transparency and usefulness of grants and contribution data and subsequently launched the Guidelines on the Reporting of Grants and Contributions Awards, effective April 1, 2018.
The rules and principles governing government grants and contributions are outlined in the Treasury Board Policy on Transfer Payments. Transfer payments are transfers of money, goods, services or assets made from an appropriation to individuals, organizations or other levels of government, without the federal government directly receiving goods or services in return, but which may require the recipient to provide a report or other information subsequent to receiving payment. These expenditures are reported in the Public Accounts of Canada. The major types of transfer payments are grants, contributions and \'other transfer payments\'.
Included in this category, but not to be reported under proactive disclosure of awards, are (1) transfers to other levels of government such as Equalization payments as well as Canada Health and Social Transfer payments. (2) Grants and contributions reallocated or otherwise redistributed by the recipient to third parties; and (3) information that would normally be withheld under the Access to Information Act and the Privacy Act.
$50,000.00
May 1, 2025
For-profit organization
AI Assist: ARP AI Qualification and Planning for Intelligent Car Buying Insights
1030580
The Project will support pilot testing of AI strategies, determine target architecture and quantitative technical objectives for implementation of an AI solution.
$120,000.00
Jun 14, 2017
Model-Based Synthesis and Safety Assurance of Intelligent Controllers for Autonomous Vehicles
RGPAS
$120,000.00
Jan 10, 2018
Model-Based Synthesis and Safety Assurance of Intelligent Controllers for Autonomous Vehicles
DGDND
$25,000.00
Sep 20, 2017
L'intelligence artificielle en appui au traitement de nuages de points LiDAR
EGP
$4,500.00
Aug 10, 2017
Développement d'un système intelligent pour détecter les conditions de givre atmosphérique
URU
$586,079.00
Apr 22, 2019
For-profit organization
Engineering Design Review Collaboration - Intelligent Issue Tracking & Interface Management Processes
926467
CoLab builds Gradient - a collaboration platform that helps teams streamline their design process and resolve issues faster. They are developing a novel file sharing and review methodology that protects IP, eliminates file duplication while enabling data-driven decisions.
$20,000.00
Jun 1, 2018
For-profit organization
Youth-Green: R&D in energy intelligence and applications in system optimization
907953
Recent Graduates (MSc/PhD) will be working on the development side of our DRAX ONE R&D program focused on creating tools and demand-side intelligence for grid-connected sustainable greenhouses.
$20,000.00
May 22, 2018
For-profit organization
Youth Green - LED Intelligent Controller and Light Engine Development & Testing
908332
The project will be directed to the development and testing of LED controllers and LED light engines. The project will include the prototype development of products and testing.
$16,500.00
Jun 5, 2019
For-profit organization
Development of the sustainable & intelligent automotive market in Europe and Asia
929782
Market PMG services to overseas automotive manufacturers for certification and research testing to Canadian and American standards. Foster the attraction of OEM R&D offices in Canada.
$200,200.00
Mar 24, 2020
Academia
Intelligent Design through Graph Generation with Deep Generative Models and Reinforcement Learning
947409
The objective of this project is to develop general machine learning techniques for graph generation, with the end application of smart design including new material discovery, advanced circuit design, and novel drug invention, amongst many others. Research will focus on deep generative models and reinforcement learning for the generation of graphs with optimized properties. The representation power of graph will be leveraged to sufficiently encode the key compositional behaviours and their interplays of the target domain, and treat developing a novel design as a new graph structure generation process with various composition constraints. The decomposition in the former can be attained through deep generative models with disentangled latent variables, and the composition search space in the latter can be effectively explored by deep enforcement learning.