Projets novateurs réalisés

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

31 133 projets complétés

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Projets par catégorie

Dynamique environnementale d’un sous-bassin de l’hydrosystème de Drâa, sud du Maroc

Le projet de la stagiaire Imane Nafouri, vise l’élaboration d’une carte de risque d’érosion hydrique de Dadès, un des principaux sous-bassins de l’hydrosystème de Drâa, situé au sud du Maroc. L’ensemble de cet hydrosystème est affecté par des évènements météorologiques extrêmes, à savoir des périodes de sécheresses sévères et quelques épisodes de précipitations soudaines et intenses. Toutefois, très peu d’études ont porté sur cet enjeu.
Les zones à risque d’érosion seront cartographiées en se basant sur les données de la télédétection (modèle numérique de terrain, images satellitaires, photographies aériennes, et images LiDAR (Light Detection and Ranging) et sur les données de terrain et de laboratoire (sédimentologie, minéralogie) qui sont en cours d’acquisition depuis l’automne 2022.
Ce projet s’inscrit d’un projet global soumis par les co-superviseurs du stage au Ministère des Affaires internationale et Francophonie suite à l’Appel à projet Québec-Royaume du Maroc 2022-2023, intitulé : Dynamiques hydro-géomorphologiques d’un socio-hydrosystème désertique

Voir la description complète du projet
Superviseur du corps professoral :

Najat Bhiry

Étudiant :

Partenaire :

Cadi Ayyad University

Discipline :

Earth science

Secteur :

Education

Université :

Université Laval

Programme :

Globalink Research Award

Application of multi-agent systems and block chains in additive manufacturing network scheduling

Additive manufacturing technologies enable facilities at any location in the global supply chain to bid for customer jobs. In this setting, job scheduling is based on due-dates, proximity to customer location, opportunities for cheaper consolidated shipments with other customers, etc. Both multi-agent systems and block chains can support scheduling for decentralized manufacturing. In this project, the student is expected to investigate how these technologies can work together and support intelligent scheduling of additive manufacturing tasks. The project will involve a broad literature review and the development of a case study with numerical modelling, implementation, and analysis.

Voir la description complète du projet
Superviseur du corps professoral :

Uday Venkatadri

Étudiant :

Partenaire :

ISEN

Discipline :

Engineering

Secteur :

Advanced Manufacturing; Information and Communications Technology; Other

Université :

Dalhousie University

Programme :

Globalink Research Award

Robust scheduling of liner shipping under disruption scenarios

This research proposes a new recovery model for addressing issues with container ship disruptions. To minimize the negative impact of these disruptions, the model formulates a mixed-integer programming approach that simultaneously considers three recovery strategies, namely vessel speeding-up, port skipping, and alternative routes.
The study clusters call-ports and utilizes port hubs to minimize transshipment costs while considering factors such as port capacity, queuing time for loading/unloading, and the economic value of cargo to identify the optimal port hubs.
The nonlinear problem is initially linearized using precise methods and then solved using CPLEX software. The results are anticipated to demonstrate a reduction in disruption losses. To account for the uncertainty in delay duration and disruption location, the study uses a robust optimization approach as a two-stage stochastic programming methodology.

Voir la description complète du projet
Superviseur du corps professoral :

Hamid Afshari

Étudiant :

Partenaire :

ISEN

Discipline :

Engineering

Secteur :

Transportation (excluding aerospace); Sustainability & the Environment; Ocean Tech

Université :

Dalhousie University

Programme :

Globalink Research Award

Hole spin in direct bandgap group IV quantum dots

This internship is to investigate the properties of charge carriers confined into nano-scale structures commonly known as quantum dots. The internship will focus on exploring a novel family of group IV semiconductors exhibiting a direct bandgap. The core paradigm here is to harness the favorable properties of hole spin in group IV quantum dots along with the efficient interaction with photons resulting from the bandgap directness to engineer new device architectures for quantum computing and quantum communication. The spin of a charge carrier is central in these technologies as it provides the mean to encode information. Thus, this research aims at modeling the properties of spins in group IV quantum dots and how resilient the spins are to electrical noise and coupling with the environment. This research will address a significant gap in the current knowledge pertaining to spin dynamics in emerging silicon-compatible materials with numerous attractive properties regrading performance and information transfer efficiency. This collaboration will thus extend the knowledge in the field of quantum technologies towards a new family of devices with properties leading to high quality spin systems and their effective interface with photons.

Voir la description complète du projet
Superviseur du corps professoral :

Oussama Moutanabbir

Étudiant :

Partenaire :

University of Basel

Discipline :

Physics

Secteur :

Quantum Science; Nanotechnology

Université :

Polytechnique Montréal

Programme :

Globalink Research Award

Assessing Snow Properties’ Impact on Various Sensors with the Snow Microwave Radiative Transfer Model

Arctic Sea-ice extent is declining due to climate change. Wildlife, maritime operations and northern communities that depend on sea-ice for traveling and hunting are negatively affected by its decline. Monitoring sea-ice thickness is an important tool to help in decision making in Arctic regions but, due to the snow covering the sea-ice, remote sensing’s sea-ice thickness retrieval methods are not yet accurate enough to assess every situation.
The Snow Microwave Radiative Transfer (SMRT) model is used to understand how snow properties impact radar scattering. This internship offers the first opportunity to assess the capacity of SMRT to simulate a frequency-modulated continuous wavelength radar responses to snow properties on sea-ice. It will also allow to evaluate the influence of snow variability and its aggregation across different footprint sizes of space born sensors, such as Sentinel-1 and CryoSat-2. This work will contribute directly to increase the accuracy of sea-ice thickness retrievals.

Voir la description complète du projet
Superviseur du corps professoral :

Alexandre Langlois

Étudiant :

Partenaire :

Northumbria University

Discipline :

Earth science

Secteur :

Environmental Science and Technology; Water; Aerospace

Université :

Université de Sherbrooke

Programme :

Globalink Research Award

A Nanomaterial-Integrated Paper Microfluidic Device for Detecting and Predicting Myocardial Infarctions

Cardiovascular disease kills 17.9 million people globally each year. 85% of these deaths were caused by either a myocardial infarction (heart attack) or stroke. Current tests for predicting heart attacks provide rapid results when presented with a blood sample, but these samples must be preprocessed using time-consuming methods. This project will develop a paper-based platform to detect heart attack markers in blood without external preprocessing. This will be accomplished by integrating a blood processing device into the testing platform. Ideally, this platform will predict heart attacks within five minutes of drawing blood from patients, facilitating life-saving medical treatment. The partner organization, Mitra Biotechnologies, previously developed a paper-based blood processing device. They are now partnering with researchers at the University of Waterloo with expertise in paper-based medical tests to turn this device into an all-in-one blood processing and testing platform that can be brought to market to save lives.

Voir la description complète du projet
Superviseur du corps professoral :

Sushanta Mitra

Étudiant :

Partenaire :

Mitra Biotechnologies Inc.

Discipline :

Life Sciences

Secteur :

Manufacturing

Université :

University of Waterloo

Programme :

Accelerate

High-threshold photonic quantum computing

Today, quantum computers of any architecture still operate in the noisy intermediate-scale quantum (NISQ) regime, with systems composed of tens to hundreds of noisy qubits that are not protected from errors. To meaningfully solve challenging problems from industries using quantum computers, we need to make a system that is large enough in scale and robust to errors. The intern will be involved in Xanadu Architecture team’s efforts in investigating promising approaches for tolerating and lowering noise and errors affecting photonic platforms. The aim is to lower the demands on hardware, shortening the time to build a universal, fault-tolerant quantum computer.

Voir la description complète du projet
Superviseur du corps professoral :

Robert Raussendorf

Étudiant :

Partenaire :

Xanadu

Discipline :

Physics

Secteur :

Information and cultural industries; Manufacturing; Professional, scientific and technical services

Université :

The University of British Columbia

Programme :

Accelerate

Détection des engins perdus pour protéger les espèces aquatiques en péril dans le golfe du Saint-Laurent en utilisant l’intelligence artificielle

La perte d’engins de pêche en mer représente un danger pour la vie marine, en particulier pour les baleines noires qui risquent de s’emmêler dans les cordages. C’est pourquoi la détection des engins de pêche abandonnés est une préoccupation majeure pour les scientifiques et les autorités maritimes. Les avancées dans l’intelligence artificielle offrent une solution prometteuse à ce problème. En utilisant des algorithmes de détection, il est possible d’identifier et de localiser les zones où les engins de pêche fantômes sont susceptibles de se trouver, ce qui facilitera les efforts de récupération. Cela permettra également de réduire le temps et les coûts nécessaires pour ces campagnes de récupération. En fin de compte, l’utilisation de l’intelligence artificielle peut aider à protéger les écosystèmes marins et les espèces qui en dépendent.

Voir la description complète du projet
Superviseur du corps professoral :

Noureddine Barka;Tawfik Masrour

Étudiant :

Partenaire :

Merinov (Gaspé, QC)

Discipline :

Engineering

Secteur :

Agriculture; Professional, scientific and technical services

Université :

Université du Québec à Rimouski

Programme :

Accelerate

AI-based decision support tool for storm damage prediction in Nova Scotia

Nova Scotia Power Inc. (NSPI) is the main provider of electricity in Nova Scotia. The largest disruptor to its system is the weather, which can lead to widespread equipment failure and outages across the grid. NSPI attempts to predict these damages before they occur to decide on the appropriate level of response and allocation of resources to restore service to their customers as soon as possible, during and after a weather event. Currently, NSPI uses a simple MS Excel-based tool to make predictions. However, its accuracy is relatively low. This project investigates whether modern AI/ML tools like Artificial Neural Networks, Random Forests, and Reinforcement Learning can be harnessed to improve the prediction accuracy of storm damage in Nova Scotia. The objective is to develop and test a prototype prediction tool that uses weather data like wind speed and direction, precipitation levels, ground thaw and foliage, as well as system information, e.g., the number of transformers and electricity poles and the distribution of customers across the province, to make systematic predictions about the damage to be experienced in different geographical regions as a result of forecasted extreme weather events.

Voir la description complète du projet
Superviseur du corps professoral :

Ahmed Saif

Étudiant :

Partenaire :

ISEN

Discipline :

Engineering

Secteur :

Artificial Intelligence; Energy and Utilities; Environmental Science and Technology

Université :

Dalhousie University

Programme :

Globalink Research Award

Investigation of Striped Bass Morone saxatilis, Atlantic Sturgeon Oxyrinchus oxyrinchus, and American Eel Anguilla rostrata movements and migrations in Annapolis River, Nova Scotia

Following the completion of the Annapolis River Tidal Power Generating Station in 1984, the abundance of many native diadromous fish species declined in the river. In 2019, new regulatory directives resulted in the abrupt termination of power generating activities, and tide gates that had barred access to the river were left open for the first time in 35 years. In a collaboration, Nova Scotia Power, Clean Annapolis River Project, and Acadia University are currently assessing the recolonization and habitat use by COSEWIC designated species of concern including Striped Bass and Atlantic Sturgeon and will soon monitor the movements of American Eel within the system. The goal of this project is to monitor river recolonization including spawning habitats and to collect data on critical habitat.

Voir la description complète du projet
Superviseur du corps professoral :

Michael Stokesbury;Trevor Avery

Étudiant :

Partenaire :

Nova Scotia Power;Clean Annapolis River Project

Discipline :

Life Sciences

Secteur :

Utilities

Université :

Acadia University

Programme :

Accelerate

Lobed Mixer and Inter-Turbine Duct Aerodynamics

The intern will assess computer-based simulation tools used by a leading Canadian aerospace organization to determine the accuracy of these tools. Specifically, the objective is to establish the extent to which these engineering tools can be used to capture the performance benefits of novel aerodynamic design strategies recently developed at Carleton University for aerodynamic shaping of inter-turbine ducts and compact exhaust diffusers, which are two components used on gas-turbine engines. Upon confirming sufficient accuracy of the simulation tools for this purpose, the partner organization will be able to integrate the research findings developed at Carleton University into the design of their gas turbine engines.

Voir la description complète du projet
Superviseur du corps professoral :

Metin Yaras

Étudiant :

Partenaire :

Pratt & Whitney Canada

Discipline :

Engineering

Secteur :

Aerospace

Université :

Carleton University

Programme :

Accelerate

Reduced order modeling and machine-learning techniques for environmental flows problem

The project involves enhancing and extending the in-house fluid flow solver using advanced mathematical and computing framework to be undertaken at the host university. The current in-house solver code can be efficiently applied to aerospace, marine and environmental flow problems. The collaboration with host university will result in a more advanced version of the code due to the implementation of mathematics based reduced order models which will speed-up the computations without any loss of physics. The reduced order modeling method has proven to reduce the complexity of the problem from millions of elements to just tens or hundreds of elements with almost similar accuracy. Another part of the research collaboration consists of application of machine learning based models to the in-house flow solver. An artificial neural network will be fed in physics of the fluid flow problem and using training data it will be trained to “learn” how to solve the physics for the particular problem. These are the main objectives of the proposed research project which will be completed in the time frame of 24 weeks under the host supervisor.

Voir la description complète du projet
Superviseur du corps professoral :

Artem Korobenko

Étudiant :

Partenaire :

Scuola Internazionale Superiore di Studi Avanzati

Discipline :

Computer science

Secteur :

Environmental Science and Technology; Artificial Intelligence

Université :

University of Calgary

Programme :

Globalink Research Award

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Mitacs est financé par le gouvernement du Canada, le gouvernement de l’Alberta, le gouvernement de la Colombie-Britannique, Research Manitoba, le gouvernement du Nouveau-Brunswick, le gouvernement de la Nouvelle-Écosse, le gouvernement de l’Ontario, Innovation PEI, le gouvernement du Québec, le gouvernement de la Saskatchewan, le gouvernement de Terre-Neuve-et-Labrador et le gouvernement du Yukon.