Grants and Contributions

This project aims to engage residents, Inuit organizations, industry leaders, builders, developers, and educational organizations on the development and integration of energy-efficient and other building technologies.

The project will develop ALEX into Docker-based microservices deployed across Kubernetes clusters to enable large-scale digital-twin simulations and reinforcement learning. A key challenge is supporting asynchronous learning across multiple parallel environments, requiring R&D in distributed stability, synchronization, and communication.

This project explores reinforcement learning (RL) to improve how those questions are ordered, so users reach relevant programs faster.

This project proposes to develop the capability to simulate the vibrational properties of 100k atom size systems using deep neural networks (DNN) methods. Such simulations will advance the ability to directly compute what is currently measured with novel Raman techniques. In this project, theoretical and experimental groups will collaborate to bring simulation tools to the next level, where generated data can be directly compared to experimental Raman spectra of complex matter. This testing platform will consist of Raman imaging of defective graphene sheets, systems that cannot be addressed with standard numerical methods because of the size of the problem. The goal is to develop AI-assisted tools to understand better the type of defects that cause a certain Raman response. These tools will use density-functional theory (DFT) simulations as input to guarantee the accuracy and predictability of the approach. These tools will help in the analysis of 2D materials. One possible use of 2D materials is in gas detection and having a better understanding of Raman signals on these surfaces before and after being exposed to certain gases will provide a guide on which materials have the better potential for this type of application.

Produce approximate (yet accurate) spectra from atomic structures using supervised learning on a large dataset of first principles-based calculations.

Prototype system for autonomous Weed Identification and Treatment System using Aerial Drones and Computer Vision.

Development of tools based on artificial intelligence technologies to assist in the analysis of component inspection data by non-destructive control.

The project involves the hiring of one PhD student. A reinforcement learning strategy for the vessel control algorithm to capture the complex interactions with ice on water is proposed.

Produce approximate (yet accurate) spectra from atomic structures using supervised learning on a large dataset of first principles-based calculations.

This project consists of offering judicial information workshops, particularly on legal instruments integrating gender, to underrepresented members of an official language minority community, in order to develop their knowledge of judicial matters and increase their access to justice. These workshops will be facilitated by subject matter experts who will convey complex information in an accessible manner, for the benefit of project participants.