Projets novateurs réalisés

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

31 620 projets complétés

2978
AB
5221
C.-B.
856
MB
696
NL
899
SK
9419
ON
9858
QC
98
PE
619
NB
1192
NS

Projets par catégorie

Market Analyst

Act as a Market Analyst to carry out:
– quantitative and qualitative research using modern and traditional methods
– analyze and interpret patterns, solutions and opportunities informed by accurate data and research
– formulate plans, strategies and opportunities for presentation to senior management
– remain informed on market trends
– evaluate market and business opportunities and make recommendations in order to identify and develop strategies to attract markets
– conduct social or economic surveys on regional, national and international markets to assess development potential and future trends

Voir la description complète du projet
Superviseur du corps professoral :

John Medcof

Étudiant :

Partenaire :

FluidAI Medical

Discipline :

Business

Secteur :

Health and Related Sciences & Technology

Université :

McMaster University

Programme :

Business Strategy Internship

Design Limits for Carbon Fibre ReinforcedPolymers

Hudson Boat Works in London, Ontario is the only company in Canada that manufactures highperformance
rowing shells. Hudson wishes to employ finite element techniques to optimize the design
of their rowing shells and associated structures using advanced engineering techniques. Optimization
of composite materials design requires a detailed understanding of their mechanical response,
including how they fail. Researchers at Western will focus on studying these failure mechanisms, and
in doing so will generate the material data required for finite element analysis. The fracture surfaces of
Hudson’s single ply materials and sandwich panels will be evaluated using optical and scanning
electron microscopy to characterize the failure mode.
The successful conclusion of this project will provide Hudson with the requisite capabilities to employ
advanced engineering techniques to optimize their composite structures. Furthermore, improved
understanding of failure mechanisms in composite materials is applicable to a wide variety of
industries with requirements for tough, lightweight materials.

Voir la description complète du projet
Superviseur du corps professoral :

Jeffrey Wood

Étudiant :

Partenaire :

Hudson Boat Works

Discipline :

Earth science

Secteur :

Manufacturing

Université :

Western University

Programme :

Accelerate

Optimisation de l’ordonnancement des activités de projet en réfection de navires.

Thales développe un logiciel pouvant gérer les activités de projet en réfection de navires militaires. En particulier, ce logiciel permet d’ordonnancer les différentes tâches à accomplir, c’est-à-dire les positionner sur une ligne du temps. L’ordonnancement produit doit tenir compte de la disponibilité des ressources (main-d’œuvre, équipements, espaces de travail) tout en minimisant les heures supplémentaires nécessaires pour respecter les échéances. La programmation par contraintes s’est déjà montrée efficace pour résoudre le problème de Thales. Cependant, lorsque le nombre de tâches devient grand, il devient impossible de le résoudre dans un temps raisonnable (quelques heures). L’objectif général du projet est donc d’accélérer le processus de résolution en intégrant une technique de recherche de solutions couramment utilisée appelée recherche à grand voisinage. Trois spécifications de cette technique ont été établies et chacune d’elles sera testée durant le stage.

Voir la description complète du projet
Superviseur du corps professoral :

Claude-Guy Quimper

Étudiant :

Partenaire :

Thales Canada Inc

Discipline :

Computer science

Secteur :

Management of companies and enterprises; Manufacturing; Professional, scientific and technical services

Université :

Université Laval

Programme :

Accelerate

Psychometric evaluation of the Show Up Questionnaire and estimation of innovation climate latent profiles

ShowUp is a new product by Dovico that provides teammates the opportunity to improve their workplace culture. The evaluation is conducted with the help of a questionnaire. Dovico aims to use the results of these questionnaires in a machine learning setting to predict innovative culture in teams and organizations.

Voir la description complète du projet
Superviseur du corps professoral :

Denis Lajoie

Étudiant :

Partenaire :

Dovico

Discipline :

Sociology

Secteur :

Information and cultural industries

Université :

Université de Moncton

Programme :

Accelerate

Sniffing Line in Mass Spectrometry for Direct Analysis

Mass spectrometry is a known for its capacity of detection of sample material with high sensitivity paired with high resolution and accuracy. Developing a “sniffing line” that would provide point-of-measurement capabilities for direct analysis of particulates on various surfaces is a necessary and novel method that will influence the mass spectrometry technique. Referred to as ‘sniffer line’, the apparatus will allow for surface contaminants to be more effectively sampled from the target surface and transported directly into the mass spectrometer. The result will be a more robust particulate analyzer providing enhanced detection ability, higher accuracy, shorter duration, decrease in cost-of-use, and real-time detection, while eliminating false-positive and false-negative errors which is an unaddressed issue in the industry. Recently, QuadroCore invented a new method to provide a more direct and sensitive detection system that detects minute traces of analytes from target materials. The goal of this project is to characterize the first “sniffing line” of this patent by researching, designing, and developing a suitable apparatus.

Voir la description complète du projet
Superviseur du corps professoral :

Qiyin Fang

Étudiant :

Partenaire :

QuadroCore

Discipline :

Engineering

Secteur :

Advanced Manufacturing; Life Sciences (not health)

Université :

McMaster University

Programme :

Accelerate

Evaluation & sensitivity study of behind-the-meter load disaggregation methods

Solar energy generation at commercial and industrial sites has been gaining popularity in recent years. The addition of batteries to existing solar installations can allow for solar to not only be used during the day when the sun is out, but also at night. There however, are various hurdles to this process specifically involving the transparency of the data coming from the solar unit. Several methods have addressed this issue at residential sites and offer promising results. However, these methods have not be tested for various geographies, climates, and operational issues that can arise at commercial and industrial sites. We will attempt to build upon residential PV disaggregation methods using both physics and statistics-based models. If successful, our method will likely allow for energy storage to be added at existing PV locations, increasing the amount of clean energy used in various industries across Canada & North America.

Voir la description complète du projet
Superviseur du corps professoral :

Andrea Scott

Étudiant :

Partenaire :

Energy Toolbase

Discipline :

Engineering

Secteur :

Professional, scientific and technical services

Université :

University of Waterloo

Programme :

Accelerate

Application of machine learning techniques to control surface quality of as-printed wire arc additive manufactured components

Nowadays, the wire arc additive manufacturing is making its path toward providing benefits to aerospace, defense, and oil and gas sectors, ascribed to the process capacity to fabricate components with minimum waste of material and lead time. However, the main challenges associated with the WAAM that have hindered the wide-spread application of the technology include the irregular and random quality of the WAAM fabricated surfaces. The mission of this project is to control aforementioned irregularities in fabrication by implementing machine learning-based algorithms and modify the process parameters to achieve a defect-free part with high surface quality. The anticipated trained machine learning method in this project will foster the progress toward completion of an autonomous in-situ defect recognition and correction (AIDRAC) system, which is the primary goal of the intern.

Voir la description complète du projet
Superviseur du corps professoral :

Ali Nasiri

Étudiant :

Partenaire :

Springboard Atlantic Inc.

Discipline :

Engineering

Secteur :

Technology; Manufacturing and Construction; Advanced Manufacturing

Université :

Memorial University of Newfoundland

Programme :

Accelerate

Discover anomaly signatures from time series data of telecommunication networks

Failures in a telecommunication network harm the communication quality. Once happened, if the system cannot solve it by self-healing, such anomaly may even result in serious problem and result in massive economic loss. In this project, we will design and develop a system to predict these failures in advance using the status values of the hardware facilities. Our goal is to build a completed data processing, model building and training system to predict facility failures automatically for production-level deployment with strict evaluation criteria (precision > 80%, which means for all the positive prediction our model gives, at least 80% of them is correct; recall > 10%, which means for all real failures in the network, our model could predict at least 10% of them). The success of this project will expand Ciena’s capability to develop superior products for anomaly prediction services.

Voir la description complète du projet
Superviseur du corps professoral :

Yan Liu

Étudiant :

Partenaire :

Ciena Corporation (Ottawa, ON)

Discipline :

Computer science

Secteur :

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

Université :

Concordia University

Programme :

Accelerate

Élaboration d’un modèle théorique valide à la base d’un moteur intelligent pour le développement professionnel en formation.

Le projet vise à développer un dispositif intelligent permettant l’autodiagnostic et l’auto-orientation de personnes formatrices qui souhaitent développer leurs compétences professionnelles et plus spécifiquement leurs compétences dans l’usage du numérique pour enseigner et apprendre. Ce dispositif innovant s’appuyera sur un moteur intelligent rigoureux fondé sur un cadre théorique en éducation validé. La stagière contribuera à la validation scientifique du modèle théorique ainsi qu’à sa mise en application dans un contexte d’autoformation. Ce projet permettra de donner une force théorique et scientifique aux développements informatiques en cours, de consolider les interactions à la base d’un robot intelligent ainsi que d’augmenter la valeur commerciale des développements et des produits de l’entreprise.

Voir la description complète du projet
Superviseur du corps professoral :

Florian Meyer

Étudiant :

Partenaire :

Optania Solutions Inc

Discipline :

Sociology

Secteur :

Professional, scientific and technical services

Université :

Université de Sherbrooke

Programme :

Accelerate

Mechanistic study of tellurium/carbon cathode in liquid and solid-state lithium-tellurium batteries

Lithium-tellurium (Li-Te) batteries provide higher volumetric energy density than current lithium-ion batteries and are considered as one of the most promising energy storage technology for emerging applications in electric vehicles, implantable medical devices, and Internet of things. The development of stable tellurium/carbon (Te/C) cathodes is the key towards durable Li-Te batteries. However, the reaction mechanism of Te/C with Li ions remain unknown due to complexity involved from the porous carbon, pore size, and the type of electrolytes, limiting the design of practical Li-Te batteries with high energy and power densities. This collaborative project between the University of British Columbia (Canada) and National Cheng Kung University (Taiwan) will fill this knowledge gap by using advanced in-situ characterization techniques to track real-time reactions between Te/ C and Li ions. The new knowledge obtained in this project will not only advance fundamental understanding on solid-state chemistry, but also accelerate the development and commercialization of Li-Te batteries for practical applications .

Voir la description complète du projet
Superviseur du corps professoral :

Jian Liu

Étudiant :

Partenaire :

National Cheng Kung University

Discipline :

Engineering

Secteur :

Clean Technology; Nanotechnology; Green/Alternative Energy

Université :

The University of British Columbia - Okanagan

Programme :

Globalink Research Award

Investigation of Water-in-Oil Emulsion on CSI Solvent Dissolution and Ex-solution Performance for Heavy Oil

This research work will establish a systematic workflow for analyzing transient equilibrium foamy oil phase behavior by coupling the CCEC tests, depletion rate and presence of water-in-oil emulsion which are seldom performed for heavy oil. It will provide a strong connection and comparison with previously studies which was conducted in the absence of water-in-oil emulsion. It will create a strong connection between phase behavior with fluid properties, operating conditions and kinetics. A concrete database which contains large amount of laboratory transient equilibrium data for selected heavy oil reservoirs can be established. The effects of different content of water-in-oil emulsion on the heavy oil production performance will be evaluated.

Voir la description complète du projet
Superviseur du corps professoral :

Na Jenna Jia

Étudiant :

Partenaire :

Petroleum Technology Research Centre

Discipline :

Engineering

Secteur :

Mining; Professional, scientific and technical services

Université :

University of Regina

Programme :

Accelerate

Promoting Gender Equality through Social Innovation

From women-only taxi companies in New Delhi to smokeless stoves in Uganda, innovation can transform the lives of women and girls around the globe. While it is well known that social innovation and women’s empowerment are each processes that drive change, there is little research to date connecting social innovation to the empowerment of women and girls. This project will identify how social innovation connects to women’s empowerment through a partnership with MATCH International. This project will support MATCH through the launch, development and growth of the Women’s Fund for Social Innovation. As the first of its kind in Canada, the fund will invest in innovation projects proposed by women and girls in the global South.

Voir la description complète du projet
Superviseur du corps professoral :

Bipasha Baruah

Étudiant :

Partenaire :

Match International

Discipline :

Sociology

Secteur :

Other services (except public administration)

Université :

Western University

Programme :

Accelerate

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Mitacs — Retour sur innovation

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.