Projets novateurs réalisés

Explorez des milliers de projets réussis issus de la collaboration entre organisations et talents postsecondaires.

31 132 projets complétés

2940
AB
5159
C.-B.
837
MB
685
NL
882
SK
9291
ON
9695
QC
97
PE
601
NB
1161
NS

Projets par catégorie

Optimizing Field Protocols for Assessing Pathogen Loads in Bumble Bees

Bumble bees are essential pollinators, but their populations are declining due to habitat loss, climate change, and pathogens like Crithidia and Vairiomorpha. These pathogens spread through feces on flowers and within colonies, reducing bee fitness and survival. While pathogen levels are well-documented in mainland southern Ontario, little is known about their prevalence on Pelee Island, a potentially important refuge for at-risk species. This project aims to assess whether Pelee Island bumble bees have lower pathogen levels, which could make the island a key site for conservation efforts. In partnership with Wildlife Preservation Canada (WPC), we will conduct surveys to measure pathogen prevalence in bumble bees across the island and compare infection rates to mainland populations. Using a non-destructive fecal sampling method developed by WPC, we will refine techniques for monitoring bumble bee health and explore behavioural assays to improve pathogen detection. The intern will also assist with WPC’s conservation breeding program to identify healthy bees for rearing efforts. This research will improve pathogen monitoring, inform pollinator conservation strategies, and determine whether Pelee Island serves as a low-pathogen refuge. Findings will support WPC’s breeding programs and broader efforts to protect imperiled bumble bee populations and maintain biodiversity.

Voir la description complète du projet
Superviseur du corps professoral :

Scott MacIvor

Étudiant :

Partenaire :

Wildlife Preservation Canada

Discipline :

Life Sciences

Secteur :

Professional, scientific and technical services

Université :

University of Toronto

Programme :

Accelerate

Pollution control of gas mixtures: gas monitoring and detection of contaminants usingnovel THz technology Year Two

Electric power plants are the number one toxic air polluters in North America. The emitted pollutants are proven to cause serious health and environmental issues. The emission of Carbon dioxide and of other pollutants, such as nitrogen oxides, sulfur dioxide – major drivers of the human-accelerated global climate change- must be monitored insitu. Our goal for the present project is to explore the properties of Terahertz radiation for control of pollution in the atmosphere. In particular, we intend to develop a new waveguide-integrated gas monitor, based on Bragg grating sensors. Such devices, whose sensitivity will be increased through modulation via an external magnetic field, will be based on probing induced anisotropy. The integration of the proposed sensor into the existing line of products of our industrial partner, QPS Photronics, can potentially result in a versatile tool for industrial applications, which could give the company a leading edge over the competition.

Voir la description complète du projet
Superviseur du corps professoral :

Roberto Morandotti

Étudiant :

Partenaire :

QPS Photronics Inc

Discipline :

Physics

Secteur :

Health and Related Sciences & Technology; Manufacturing

Université :

Université du Québec : Institut national de la recherche scientifique

Programme :

Elevate

Thermally Driven Contactless Power Transfer Using Thermophotovoltaic Receivers

The objective of the proposed research project is to build and demonstrate a fuel-powered contactless power transfer (CPT) system. This system is comprised of two units: an emitter and a receiver. The emitter, which can be heated using different fuels (including oil, gasoline, hydrogen, biodiesel) emits radiant energy over a distance to the receiver, which converts the radiant energy to electric power using photovoltaic cells. The emitter and receiver units can be mounted on different vehicles to achieve mobile CPT. This CPT system can be used in energy networks supporting a range of vehicles and infrastructure in remote and harsh environments such as arctic regions and outer space. This CPT system will exhibit significant advantages over existing wireless power transfer system. Since it is powered by fuel instead of batteries it can provide for much higher energy densities and reliability when operating in cold environments.

Voir la description complète du projet
Superviseur du corps professoral :

Paul O'Brien

Étudiant :

Partenaire :

Columbiad Launch Services

Discipline :

Engineering

Secteur :

Professional, scientific and technical services

Université :

York University

Programme :

Accelerate

Vision-based precision landing for UAVs

This project aims to develop a robust vision-based precision landing system that enables drones to dock accurately in challenging environmental conditions including rain, fog, and darkness. The system leverages computer vision technology mounted on an ARA-408 drone to identify visual markers on a docking station, calculate trajectory, and execute precise landings when traditional GPS guidance may be compromised. This collaborative effort between ARA Robotique and Polytechnique Montreal represents an advancement in autonomous drone capabilities for monitoring applications, ensuring reliable operations in environments where conventional navigation systems might fail.

Voir la description complète du projet
Superviseur du corps professoral :

Giovanni Beltrame

Étudiant :

Partenaire :

ARA Robotique

Discipline :

Computer science

Secteur :

Manufacturing; Professional, scientific and technical services

Université :

Polytechnique Montréal

Programme :

Accelerate

Debugging the Brain: Reading and Writing Human Cortex using Electroencephalography (EEG) and Transcranial Magnetic Stimulation (TMS)

The Temerty Centre for Therapeutic Brain Intervention aims to find new understandings of and approaches for hard-totreat mental illnesses across patients’ lifespans by delivering high quality clinical care and research using brain stimulation and psychedelic therapies. Personalized modulation of brain networks with Transcranial Magnetic Stimulation (TMS) is a promising treatment for neuropsychiatric brain disorders such a major depressive disorder. However, there is a need to
better optimize the treatment, to increase patient’s quality of life and reduce healthcare costs (Fitzgibbon et al., 2020). To do so requires the ability to reliably decode the brain activity evoked by TMS, as measured by Electroencephalography (EEG). However, it is challenging to analyze this EEG data because it has high dimensionality (an EEG recording from one patient contains thousands of data points for each of the 64 electrodes in an EEG cap) and noise (patient movements such as eye blinking can introduce artifacts in the signal (Louis et al., 2016)). EEG data analysis for TMS sessions is further complicated because, to extract relevant insights from a single trial data of EEG data, prior information on the expected signal features is required, yet currently not known (Hernandez-Pavon et al., 2023).

Voir la description complète du projet
Superviseur du corps professoral :

Arvind Gupta;Huaxiong Huang

Étudiant :

Partenaire :

Centre for Addiction and Mental Health

Discipline :

Computer science

Secteur :

Health and Related Sciences & Technology; Professional, scientific and technical services

Université :

University of Toronto

Programme :

Accelerate

Implementation of Software Features for Electrochemical Impedance Spectroscopy Analysis in Industrial Electrochemical Assets

Pulsenics will collaborate closely in this project by partnering with the intern to enhance their software platform, specifically targeting real-time Electrochemical Impedance Spectroscopy (EIS) analysis. Pulsenics faces challenges with their current EIS software, which lacks the efficiency and diagnostic accuracy needed for effective real-time monitoring, resulting in increased downtime and less-than-optimal asset management. This research aims to develop a modular solution that addresses these shortcomings by improving diagnostic accuracy and reducing maintenance interruptions.
Pulsenics is an OEM specializing in EIS solutions for R&D, production QA/QC, and real-time monitoring of industrial-scale electrochemical systems. Their integrated hardware and software platforms support high-performance measurement, data analysis, and system control across a broad range of applications—including electrolyzers, fuel cells, batteries, electrochemical water treatment, and metallurgy. Pulsenics’ technology is used globally to enable innovation, scalability, and reliability in the transition to electrochemical-based systems.
As industries rapidly scale green technologies, there is a critical need to shift from R&D-centric workflows to scalable, production-ready operations. This transition demands faster iteration cycles to develop and validate new technologies, and robust, automated QA/QC systems that derisk sales while maintaining high throughput. This project will accelerate development, streamline QA/QC, and help deploy production-scale electrochemical systems with greater confidence and less manual intervention.

Voir la description complète du projet
Superviseur du corps professoral :

Wai Tung Ng

Étudiant :

Partenaire :

Pulsenics

Discipline :

Computer science

Secteur :

Professional, scientific and technical services

Université :

University of Toronto

Programme :

Accelerate

Enhancing DAG Deployment and Testing through User-Centered Design

Geotab, the global leader in connected vehicle and asset solutions, leverages advanced data analytics and AI to enhance fleet performance, safety, and sustainability while optimizing costs. Backed by a team of industry leading data scientists, engineers, and AI experts, we serve over 50,000 customers
across 160 countries, processing billions of data points hourly from more than 4 million vehicles.
The Geotab Data Platform team is responsible for enabling and empowering the work of data scientists and developers by providing a platform for data ingestion, orchestration, digestion, and all applications of data. The sheer quantity of data that flows Geotab requires significant engineering hours working towards deployments, environment configuration, error handling, anomaly detection, and workflow performance optimizations. The goal of this project is to explore and develop automation tooling leveraging AI to increase efficiency, reduce manual effort, reduce errors, and identify ways to improve performance within the system.
Improvements to all the above makes impacts throughout Geotab, improving the quality of the solutions provided, as well as increasing the throughput of data, allowing Geotab to serve more customers and bring in more revenue.

Voir la description complète du projet
Superviseur du corps professoral :

Azadeh Farzan

Étudiant :

Partenaire :

Geotab Inc

Discipline :

Computer science

Secteur :

Information and cultural industries; Professional, scientific and technical services; Transportation and warehousing

Université :

University of Toronto

Programme :

Accelerate

Structural brain connectivity in predicting treatment outcomes in major depression

(1) CAMH is a research hospital that aims to improve the provision of clinical care to clients with psychiatric conditions through research. Through analyses of large-scale multi-modal data and clinical trials with biological assessments, the Kimel TIGR lab seeks to identify biological bases of psychiatric conditions that can be targeted using existing and novel treatments, such as SSRIs, SNRIs, repeated TMS, and iTBS, among others.
(2) This project aims to a) develop novel machine learning tools that link structural brain connectivity to cognitive function and clinical symptoms in several datasets, starting with the large-scale UK Biobank data (63k participants with MRI). Additionally, b) the project will test the performance of graph neural nets alongside other machine learning tools in predicting treatment outcomes in clinical trials for depression treatment.
(3) If successful, this work may lead to wide-spread adoption of graph neural networks in neuroscience. In the longer term, if confirmed by prospective biomarker-guided clinical trials such as those that Dr. Zhukovsky is currently involved in (SMART Trial, McLean hospital with Prof Pizzagalli, Wellcome Leap funded; co-leading biomarker search as part of the BAARD trial across CAMH, Pittsburgh and Washington University St Louis, NIH funded with Dr. Felsky), biomarker-guided algorithms could help target medications for depression treatment.

Voir la description complète du projet
Superviseur du corps professoral :

Hans-Arno Jacobsen

Étudiant :

Partenaire :

Centre for Addiction and Mental Health

Discipline :

Life Sciences

Secteur :

Health and Related Sciences & Technology; Professional, scientific and technical services

Université :

University of Toronto

Programme :

Accelerate

From Slow to Fast Thinking for LLMs

The partner, Boson AI Inc., is a leading AI solutions provider, specializing in customized model serving for businesses. Through this project, Boson AI aims to tackle key challenges in improving the reasoning capabilities of large language models (LLMs).
While techniques like Chain-of-Thought prompting have shown promise in improving LLM performance on complex tasks, they remain limited in scope and introduce significant computational and latency overhead. This project seeks to improve reasoning efficiency by leveraging recent advances in LLM fine-tuning and alignment, enabling faster, more efficient inference without compromising reasoning accuracy.
The anticipated benefits for Boson AI include significant cost savings, higher customer satisfaction, and enhanced regulatory compliance. Ultimately, the project’s success is expected to strengthen Boson AI’s position as a leader in scalable and efficient AI systems, while advancing its broader research strategy in next-generation generative models.

Voir la description complète du projet
Superviseur du corps professoral :

Xujie Si

Étudiant :

Partenaire :

Boson AI

Discipline :

Computer science

Secteur :

Education; Information and cultural industries

Université :

University of Toronto

Programme :

Accelerate

Collision (or crash) Severity Inference

Geotab is a global leader in IoT and connected transportation, providing valuable insights to over 80,000 customers worldwide by collecting more than 4 billion data points daily. The Safety & Video Analytics team at Geotab currently detects vehicle collisions using in-house machine learning models on telematics data. However, the severity of crashes is currently assessed solely through g-force measurements, which is not always reliable.
This project aims to develop a more sophisticated method to infer crash severity using additional data sources, such as kinematic data, contextual data lakes, and external crash databases (e.g., Federal Motor Carrier Safety Administration – FMCSA). The new methodology will help in collision reconstruction, insurance claim processing, and overall vehicle safety analysis. By providing a more precise and data-driven severity assessment, Geotab can enhance its services for customers and insurance partners, leading to better risk evaluation, pricing, and claims management. The success of this project would help Geotab provide more contextual information about a crash to the customers that can be used for planning and for litigation purposes. This will also help us build more strategic partnerships by providing more value to our insurance partners

Voir la description complète du projet
Superviseur du corps professoral :

Andrei Badescu

Étudiant :

Partenaire :

Geotab Inc

Discipline :

Computer science

Secteur :

Information and cultural industries; Professional, scientific and technical services; Transportation and warehousing

Université :

University of Toronto

Programme :

Accelerate

Pollution control of gas mixtures: gas monitoring and detection of contaminants usingnovel THz technology

Electric power plants are the number one toxic air polluters in North America. The emitted pollutants are proven to cause serious health and environmental issues. The emission of Carbon dioxide and of other pollutants, such as nitrogen oxides, sulfur dioxide – major drivers of the human-accelerated global climate change- must be monitored insitu. Our goal for the present project is to explore the properties of Terahertz radiation for control of pollution in the atmosphere. In particular, we intend to develop a new waveguide-integrated gas monitor, based on Bragg grating sensors. Such devices, whose sensitivity will be increased through modulation via an external magnetic field, will be based on probing induced anisotropy. The integration of the proposed sensor into the existing line of products of our industrial partner, QPS Photronics, can potentially result in a versatile tool for industrial applications, which could give the company a leading edge over the competition.

Voir la description complète du projet
Superviseur du corps professoral :

Roberto Morandotti

Étudiant :

Partenaire :

QPS Photronics Inc

Discipline :

Physics

Secteur :

Health and Related Sciences & Technology; Manufacturing

Université :

Université du Québec : Institut national de la recherche scientifique

Programme :

Elevate

Agent Learning in SLMs

The partner organization Layer 6 AI is a leading artificial intelligence research lab and a part of TD Bank Group. It focuses on advancing machine learning and deep learning technologies to drive innovation across various sectors including financial services. The company specializes in AI-driven solutions such as predictive modeling, recommendation systems, and natural language processing. With a commitment to cutting-edge research, Layer 6 collaborates with academia and industry to push the boundaries of AI applications.
In the financial services industry, a key challenge lies in managing and retrieving information from vast and heterogeneous knowledge repositories. Furthermore, operational efficiency hinges on automating complex, multi-step workflows. Large language models have the capacity to independently plan, reason, and interact with various tools, which provides a significant opportunity to streamline knowledge management and enhance workflow automation, all while operating within the necessary compliance boundaries.
Through this collaboration, the partner stands to gain strategic benefits. The insights from agent learning in small language models could dramatically improve workflow automation, reducing operational overhead and improving service quality. The project will also help the partner develop in-house expertise on cutting-edge AI techniques, enabling faster and more efficient deployment of language-driven solutions across multiple business units.

Voir la description complète du projet
Superviseur du corps professoral :

Rahul G. Krishnan

Étudiant :

Partenaire :

Layer 6 AI

Discipline :

Computer science

Secteur :

Finance and Insurance; Professional, scientific and technical services

Université :

University of Toronto

Programme :

Accelerate

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