Grants and Contributions

Automation in chemistry labs expedites experimentation and minimizes errors, facilitating more cost-effective material and drug discovery. The project aims to automate chemistry labs by developing transferable, adaptable, and generalizable robotic systems using Reinforcement Learning (RL). The project is designed to tackle the challenges of integrating robotic arms into chemistry labs, focusing on minimizing data requirements and enhancing real-world applications of RL. Key objectives include developing a model-based RL method that generalizes across different robotic platforms, creating robust and transferable RL agents, and ensuring the real-time adaptability of robots to evolving lab tasks. The project addresses the unique challenge of manipulating transparent objects and aims to implement high frequency control for agile and responsive robot behavior. By extending the Remote-Local Distributed (ReLoD) system to model-based RL, the project aims to refine the real-time learning and control of lab robots, setting the stage for widespread adoption of automated, efficient, and intelligent lab systems.

This study will investigate interfaces and systems for quantum communication satellites, with two main research thrusts. First is to prepare the groundwork for a link from a Quantum Ground Station (QGS) with the anticipated QEYSSat mission at three ground station sites: the University of Waterloo’s QGS (QGS-UW), the DLR-operated QGS (QGS-OGSOP), and the primary QGS at CSA in St-Hubert (QGS-SHUB). At QGS-SHUB and QGS-UW, this work includes the definition of optical interfaces, mode of operations, transmitter beam quality/wavefront analysis, exchange of data, definition of a channel metrology setup, as well as specifications of ground?based sources and networks. Paper studies on channel characterization will be conducted at QGS-OGSOP. The second is to conduct atmospheric channel and site characterization to study light pollution and how the atmosphere affects the quantum channel at QGS-SHUB and QGS-UW. These studies will lay the ground work for potentially connecting any combination of these sites in the future.

The project will support the integration and miniaturization of optically detected magnetic resonance (ODMR) technology on silicon to bring it to a technological maturity level that enables mass production compatible with the semiconductor industry. This one-year quantum sensor project is dedicated to de-risking this challenging technology transfer venture by directly addressing potential major scientific roadblocks, such as the impact of nanofabrication on sensitivity and to assess the technology readiness for a full transfer from the lab to the industry. In addition to miniaturization, the project aims to achieve a significant increase in sensitivity compared to commercially available products. The conclusion of this project will have a significant impact on research partners and the quantum industry in Canada as a whole.

The project aims to increase metallographic laboratory autonomy and facilitate the development and optimization of processes, such as high-pressure vacuum casting (HPVDC) of aluminum, cold spraying of aluminum (CSAM) and steel made by selective laser melting (SLM), by applying generative artificial intelligence (AI) models. The project will focus first on increasing efficiency by quickly and cost-effectively generating machine learning datasets with adjustable features, making it possible to generate aluminum and steel microstructures with variations in process parameters and sample preparation conditions.

This project focuses on the investigation, development, and advancement of enabling technologies related to conformal reconfigurable intelligent surfaces and beamforming antenna technologies to enhance the performance of millimeter-wave wireless links, particularly in non-line-of?sight scenarios.

This project focuses on the investigation, development, and advancement of enabling technologies related to conformal reconfigurable intelligent surfaces and beamforming antenna technologies to enhance the performance of millimeter-wave wireless links, particularly in non-line-of?sight scenarios.

To address the highlighted challenges, the project will develop progressive deep learning approaches for novel materials discovery in two stages.

Advances exploratory research under the New Beginnings Initiative

The main objective of this research is to support decarbonisation of the construction industry by mainly moving the industry toward modular and offsite construction (MOC), carbon-less materials and implementation of circular economy to significantly reduce carbon emissions, electricity consumptions, material waste, and improve safety and productivity in MOC supply chain network. This will involve: (1) developing smart and green MOC supply chain scheduling and planning system; (2) evaluating precast concrete mixtures used in MOC to identify opportunities to reduce the embodied carbon from the materials; (3) developing BIM-based design system for manufacturing and disassembly (DfMD) supporting to design, construction, and deconstruction; and (4) developing digital twin of process and products for the carbon-less MOC supply chain network and management.

Advances exploratory research under the New Beginnings Initiative