Xanadu’s mission is to build quantum computers that are useful and available to people everywhere. Our company was founded in 2016, and has grown to approximately 200 employees today, with headquarters in Toronto, Canada. Xanadu develops the full stack of quantum computing (QC) technology: theory, hardware, cloud service, software and applications. This project is in […]

This Mitacs internship program is targeted to recruit a top mechanical engineering student to research and design a state-of-the-art braking system for the Greenheart’s high-speed ziplines. The intern will conduct literature review of different braking systems used in the zipline industry and develop robust numerical models to examine the performance of existing designs. Using the […]

Electromagnetic (EM) methods are commonly used for geophysical exploration in various applications such as mineral exploration, hydrocarbon detection, management of fresh and salt water and CO2 and reservoir monitoring. While in the past, EM methods suffered from expensive data collection, new systems now collect massive amounts of data over space and time, and new instrumentation […]

It is estimated that over 12 million tons of plastic enter our oceans annually, which slowly break down creating microplastics less than 5mm in size. In the past decade, the role microbes play in this degradation has been explored as they possess the ability to metabolize plastic, using it as an energy source. In this […]

Research focused on the history of Latin American animation, from its inception, exploring international collaborations, and concluding in contemporary animation.

(1) Moleculex is dedicated to transforming traditional internal wound and trauma management and closure with safe and effective bioadhesive and mucoadhesive technology. (2) The core technology at Moleculex relies on natural-based polymers, which are limited by their inherent properties, through artificial synthesis. (3) The new polymer expected to be developed can be integrated into our […]

The medical cannabis industry in Canada is at the forefront of cannabis research, yet it faces significant challenges due to the plant’s complex chemical composition, including numerous cannabinoids, terpenes, flavonoids, and other bioactive compounds. Currently, cannabis prescriptions often rely on limited clinical data, leading to inconsistent therapeutic outcomes. This project addresses these challenges by developing […]

In many applications, Machine Learning (ML) predictions are used to make downstream decisions. Acting on ML predictions however can change the distribution of features that the ML model relies on for predictions. The implication is that such downstream decisions procedures implicitly expect the ML model to generalize outside of the observational distribution. Unfortunately, this is […]

Severe sepsis strikes young and old alike with an increasing incidence of >75,000 per year in Canada at a cost of $40,000 per patient. In 40% sepsis is complicated by low blood pressure and organ failure with a mortality rate of 30-60%. The number of deaths due to severe sepsis and septic shock is greater […]

Patients with chronic obstructive pulmonary disease (COPD) often experience adverse health outcomes including COPD exacerbations and cardiovascular events. These events impose a significant economic burden on the healthcare system and lead to impairment of patients’ wellbeing. This project aims to develop a clinical prediction model (CPM) to predict the risk of COPD exacerbations and cardiovascular […]

This project will explore how wood pellets can be used more widely as an affordable, sustainable heating option in the Northwest Territories (NWT). While some areas already use wood pellets, there’s potential to expand this environmentally friendly alternative to fossil fuels like oil and diesel. The research will involve gathering information, working with local communities, […]

Machine Learning (ML) models are known to leak information about the data they were trained on, enabling membership and reconstruction attacks [7]. Such privacy risks damage trust in ML, and hinder the broad adoption of model co-training, through Federated Learning or cloud-based co-training. This is especially true in sensitive domains that could significantly benefit from […]