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
Jul 14, 2016
$164,000.00
Oct 30, 2014
$50,000.00
Apr 19, 2017
$55,700.00
Oct 30, 2014
$198,576.00
May 31, 2012
New CA over $25,000.
$400,000.00
Dec 20, 2021
Academia
Space STEM activities for Lunar Robotics and AI
22STEMCUWL
“Space STEM Activities for Lunar Robotics and AI” will aim to deliver interactive experiences to youth (grades 6 to 12 - Cégep 1 in Québec) across all Canadian provinces and territories through multiple streams: in-class workshops and activities, virtual presentations, and educational and interactive website, teacher training workshops, camp programs, and an adaptation of the Mission Control Academy. These hands-on learning experiences will educate youth on the use of AI-enabled robotics such as machine learning, career options in AI, and the development of robotic arm missions.
$0.00
Apr 22, 2024
For-profit organization
CSJ 2024 - Metaltek Machiining Ltd
019662055
Through the application of national and local priorities, the CSJ program seeks to provide youth, particularly those who face barriers to employment with access to work opportunities. Funded employers must demonstrate that they are providing quality work experiences for youth that provide opportunities to develop and improve their skills.
$160,600.00
Apr 22, 2020
Academia
AI based shape optimization
948443
Often numerical simulations can be performed based on physical laws to virtual test the effectiveness of a design. Recent advances in machine learning allow for a data-driven solution: a neural network can be trained using pre-computed simulation data and then use the neural network as an online simulator. This project investigates geometric techniques for representing and processing shape data for machine learning and computing optimal shapes for design.
$462,750.00
Sep 1, 2020
For-profit organization
Germany BMBF Leading-Edge Cluster Project EASY - Embedded Artificial Intelligence for Production Systems
955912
Germany BMBF Leading-Edge Cluster project EASY led by Fraunhofer-Institut für Entwurfstechnik Mechatronik IEM
The present project aims to develop decentralized machine learning (ML) algorithms for embedded and edge devices. The use cases considered in this project are the intelligent setting of profile wrapping machines and the predictive maintenance of failure-critical components in conveying applications, such as, the slip of friction wheels and drive belts due to wear or damage to bearings and gearboxes. A key goal of this project is therefore the development and implementation of learning processes in distributed, embedded systems, taking into account resource constraints.
$160,600.00
Apr 22, 2020
Academia
AI based shape optimization
948443
Often numerical simulations can be performed based on physical laws to virtual test the effectiveness of a design. Recent advances in machine learning allow for a data-driven solution: a neural network can be trained using pre-computed simulation data and then use the neural network as an online simulator. This project investigates geometric techniques for representing and processing shape data for machine learning and computing optimal shapes for design.