Projets novateurs réalisés

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

31 915 projets complétés

3006
AB
5256
C.-B.
878
MB
703
NL
913
SK
9488
ON
9950
QC
98
PE
629
NB
1215
NS

Projets par catégorie

The intelligent virtual agent as a patient Personalized companion

In this project the intern will develop an intelligent virtual concierge and integrate it with Curatio’s social network platform. The intelligent concierge will learn about each user over time and act as a personal assistant of the user in using the social network platform.

Voir la description complète du projet
Superviseur du corps professoral :

Russell Greiner

Étudiant :

Partenaire :

RxPx Health

Discipline :

Engineering

Secteur :

Information and Communications Technology; Health and Related Sciences & Technology; Life Sciences (not health)

Université :

University of Alberta

Programme :

Accelerate

Vulnérabilité des populations de crevette nordique aux changements climatiques et globaux le long de la côte Est du Canada

La crevette nordique est une des plus importantes espèces exploitées à l’est du Canada, moteur d’approvisionnement et de développement pour de nombreuses communautés côtières. Depuis quelques années, les stocks de crevette nordique semblent en déclin. Le réchauffement, l’acidification et la désoxygénation des océans pourraient venir d’autant plus affecter la viabilité et la rentabilité de cette pêcherie. Cependant la vulnérabilité relative des différentes populations n’est que peu connue et ne permet pas de prédire l’évolution globale de cette pêcherie dans un contexte de changements globaux. En menaçant la ressource, ces changements pourraient à moyen et long terme impacter par extension l’activité économique de la pêche et la vitalité socio-économique de plusieurs régions atlantiques. TO BE CONT’D

Voir la description complète du projet
Superviseur du corps professoral :

Piero Calosi;Fanny NOISETTE;Marco ALBERIO;William Cheung

Étudiant :

Partenaire :

Ouranos Inc;Merinov (Rimouski, QC)

Discipline :

Life Sciences

Secteur :

Accommodation and food services; Agriculture; Professional, scientific and technical services; Public administration

Université :

Université du Québec à Rimouski

Programme :

Accelerate

Evaluation of targeted alpha-therapy on patient-derived Glioblastoma cells

Glioblastoma multiforme (GBM) is the deadliest form of human brain tumors, systematically recurring despite multimodal treatment. As a consequence, the average patient survival is less than 15 months, and is thought to be linked with the presence of brain tumor stem cells (BTSCs) that are implicated in treatment resistance. GBM BTSCs are radiotherapy and chemotherapy resistant, and BTSCs escaping treatment may explain tumor relapse. In the current era of precision medicine, targeted radiotherapy constitutes an attractive strategy for treating intractable disease. By combining the selectivity of immunotherapies with the proven lethality of radio-isotopes we have the opportunity to deliver irreversible damage to a defined cancer cell population while sparing normal tissue. We have developed an in vivo mouse-model that has the distinct advantage of generating recurrent, human, treatment-refractory GBM. TO BE CONT’D

Voir la description complète du projet
Superviseur du corps professoral :

Sheila Kumari Singh

Étudiant :

Partenaire :

Longbow Therapeutics Inc

Discipline :

Life Sciences

Secteur :

Professional, scientific and technical services

Université :

McMaster University

Programme :

Accelerate

Enhanced Techniques for History Matching and Forecasting of Petroleum Reservoir Data – Year Two

History matching refers to calibrating numerical or analytical models by the observed data. However, this task can be very challenging in presence of complex geology and/or many unknown data .
The purpose of this project is to introduce and apply the new techniques for efficient creation of predictive history-matched models for reservoir characterization of conventional and unconventional reservoirs, which can be used for probabilistic forecast and uncertainty quantification. It is expected to implement as set of code and introduce new workflows that can enhance the history matching task in various problems. This include the use and applications of the state-of-the-arts methods that can represent the geology and can efficiently and accurately calibrate the dynamic models by minimizing the computational cost.
This postdoctoral program provides a unique opportunity to further my studies in history matching and uncertainty quantification to a new level within Rock Flow Dynamics (RFD). This project helps me utilize interactions with industry and receive industrial feedback on the practicality of my algorithms.

Voir la description complète du projet
Superviseur du corps professoral :

Mario Costa Sousa

Étudiant :

Partenaire :

Rock Flow Dynamics Inc;University of Calgary

Discipline :

Earth science

Secteur :

Professional, scientific and technical services

Université :

University of Calgary

Programme :

Elevate

Enhanced Techniques for History Matching and Forecasting of Petroleum Reservoir Data

History matching refers to calibrating numerical or analytical models by the observed data. However, this task can be very challenging in presence of complex geology and/or many unknown data .
The purpose of this project is to introduce and apply the new techniques for efficient creation of predictive history-matched models for reservoir characterization of conventional and unconventional reservoirs, which can be used for probabilistic forecast and uncertainty quantification. It is expected to implement as set of code and introduce new workflows that can enhance the history matching task in various problems. This include the use and applications of the state-of-the-arts methods that can represent the geology and can efficiently and accurately calibrate the dynamic models by minimizing the computational cost.
This postdoctoral program provides a unique opportunity to further my studies in history matching and uncertainty quantification to a new level within Rock Flow Dynamics (RFD). This project helps me utilize interactions with industry and receive industrial feedback on the practicality of my algorithms.

Voir la description complète du projet
Superviseur du corps professoral :

Mario Costa Sousa

Étudiant :

Partenaire :

Rock Flow Dynamics Inc;University of Calgary

Discipline :

Earth science

Secteur :

Professional, scientific and technical services

Université :

University of Calgary

Programme :

Elevate

Exploring optimal trading rules in a high-frequency portfolio

Given a set of financial instruments with inherent characteristics at different time intervals, we are interested in finding an optimal trading rule in a high-frequency trading context. A trading rule is defined as a combination of indicators as well as an entry threshold (and potentially other trading parameters). The objective function we are trying to maximize is the profits of the strategy based on the trading rule. One impact of the non-linearity of such problems is that the gradient of the objective function is hard to estimate using a black-box approach. High frequency tick data are used so a high volume of samples is available and this fact compounds the problem. In this project, we set to explore different methods to perform an efficient random search such as the use of low discrepancy sequences, simulated annealing and evolutionary procedures. TO BE CONT’D

Voir la description complète du projet
Superviseur du corps professoral :

Manuel Morales

Étudiant :

Partenaire :

Squarepoint Technologies

Discipline :

Mathematics

Secteur :

Finance and Insurance

Université :

Université de Montréal

Programme :

Accelerate

Automated transaction classification using machine learning algorithm

The procurement process of an organization is key to understand company costs. Organizations gather large amounts of data coming from different sources (e.g. income statement, balance sheet, general ledger lines). This information is heterogeneous in nature as it is a mix of unstructured and structured data. Moreover, it needs to be cleaned and consolidated in a taxonomy to enable category management. The objective is to group like-to-like items and/or services into categories from Supply Market Analysis point of view and consider category management for the holistic spend. Supervised and unsupervised machine learning algorithm seemed to be natural choices for this kind of problem because of the nature of the available data. PwC has already a first iteration of a classification product, dubbed SAM (Spend Analysis Machine) and it is based on supervised learning for text classification on general ledger accounts and supplier characteristics. TO BE CONT’D

Voir la description complète du projet
Superviseur du corps professoral :

Maciej Augustyniak;Manuel Morales;Manuel Morales;Maciej Augustyniak

Étudiant :

Partenaire :

PwC Management Services LP

Discipline :

Mathematics

Secteur :

Professional, scientific and technical services

Université :

Université de Montréal

Programme :

Accelerate

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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.