Projets novateurs réalisés

Medventions: Winter 2025

Medventions is a full-time 14 week paid program that allows students and new graduates to take part in physician-led,
hospital-based fellowship training. Fellows come from different areas of study and will get hands on experience in a
clinical setting with innovators and experienced mentors. It equips fellows to identify priority healthcare needs and
develop their own prototypes to help solve pressing issues in our healthcare system. At the end of the program, the
interns will present their final innovation at the Innovation Showcase, which will celebrate the interns and the work
they have done over the past four months. Here, they will present their prototype and business model, and field any
questions from an audience comprised of those engrained in the health innovation ecosystem in Nova Scotia.
This cohort will be comprised of 4 fellows and our academic supervisor for this cohort is Clifton Johnston from
Dalhousie University.

Evaluating and Refining Regional Fund Allocation Models: Impact of Residential Schools and Global Comparisons

The research project will evaluate how the FGF/NIBTF distributes funds to Indigenous communities across Canada. Currently, the allocation is based on several factors, including the number of residential schools in each region. The intern will analyze historical data to see how this factor affects funding and whether changes to the formula would create a fairer distribution. By running different models, the intern will show if removing or adjusting the “residential schools” factor impacts the amount of money each region receives. The intern will also compare the FGF/NIBTF’s funding methods with those used in similar programs globally. This research will help the FGF/NIBTF ensure that their funding approach continues to meet the needs of Indigenous communities in a fair and reasonable way.

gestion stratégique des compétences dans une PME de services d’inspection de matériaux et de contrôle de qualité: recherche-action sur ses modalités d’implantation

Le succès commercial et la pérennité sociale d’une PME – bref, son âme – résident dans ses personnes et leurs compétences. Une gestion stratégique efficace des compétences d’une PME passe donc nécessairement par la bonne gestion stratégique de ses personnes ou, plus concrètement, des façons dont ces derniers décident librement et en toute conscience de mettre à contribution leurs compétences au bénéfice de l’entreprise (c.-à-d. expertise, conscience professionnelle, mécanismes personnels de création de sens). Hélas, les dimensions sensible et subjective – bref, humaines – de ce type novateur de gestion demeurent volontairement muselées et opprimées par les communautés scientifiques et d’affaires. C’est pourquoi cette recherche entreprend d’implanter dans une PME d’exception et en pleine croissance, d’une part, une gestion des personnes érigées sur les meilleures pratiques qui, d’autre part, sera bonifiée d’un cadre théorique novateur et inclusif (c.-à-d. qui considère l’ensemble des écrits disponibles sur cette thématique). Au final, la PME en question jouira de l’implantation réussie d’une gestion stratégique efficace de ses compétences, matériau vital à sa croissance, à son succès et à sa pérennité en affaires.

L2M-Unobtrusive photodetectors and imagers based on transparent semiconductive nanotube membranes

The proposed project focuses on developing unobstructive photodetectors and imagers technology using advanced semiconductors for discreet surveillance applications. By leveraging expertise in sensor technology, the project aims to create inconspicuous imaging solutions integrated into everyday items, addressing privacy concerns associated with traditional photodetectors. The partner organization stands to benefit from the innovative and ethically responsible technology, offering a competitive edge in the market and fostering public trust through transparent and covert surveillance capabilities.

Exploring quantum machine and deep learning for malware detection

This project aims to enhance malware detection capabilities through the use of quantum neural networks (QNNs) and quantum machine learning (QML), leveraging quantum computing principles for superior performance compared to classical neural networks. As cyber threats become increasingly sophisticated, QNNs and QML present promising opportunities for breakthroughs in identifying and analyzing malicious activities.
We will implement QNN models using open-source quantum computing frameworks, enabling us to explore the unique advantages of quantum computing, which may lead to more efficient data processing. The project will involve curating diverse malware datasets, ensuring coverage of various types of malware and attack vectors, followed by necessary preprocessing to prepare the data for training.
Once the datasets are ready, we will train the QNN models while experimenting with different architectures and hyperparameters to optimize performance. We will then compare the performance of the QNN models against classical methods using metrics such as accuracy, precision, recall, and F1-score. Ultimately, this project aims to demonstrate how QNNs and QML can significantly improve current malware detection techniques, integrating these advancements into existing cybersecurity frameworks to enhance resilience against evolving cyber threats.

Création d’un modèle d’encodage XML-TEI pour les registres de comité de la Comédie-Française (1677-1919)

Ce projet de recherche a pour objectif de créer une édition numérique des registres de comités de la Comédie-Française. Ces registres témoignent de la vie quotidienne de la troupe de théâtre, et sont actuellement conservés à la bibliothèque-musée de la Comédie-Française. Grâce au travail des étudiants et étudiantes de master sous la direction de Florence Naugrette et Sara Harvey, nous disposons de transcriptions pour l’ensemble des registres du 17ème et 19ème siècle, ainsi qu’une partie des registres du 18ème siècle.
Le travail qui sera effectué à l’université de Victoria, et plus précisément au HCMC sera de convertir ces transcription en XML-TEI, un schéma d’encodage qui permet de structurer des documents. Cela permettra à terme de consulter le contenu des registres sur le site internet du programme RCF, mais aussi d’effectuer des recherches par mots-clefs dans l’ensemble des registres.

Integrated Digital Communications Strategy & Implementation

The Integrated Digital Communications Strategy & Implementation project aims to enhance and improve Tutor Teach’s current digital marketing and social media presence by developing and testing a comprehensive, data-driven strategy. Through research and innovative approaches, the intern will focus on audience segmentation, platform-specific content creation, and targeted digital advertising campaigns. This project will address Tutor Teach’s current gaps in online customer acquisition by implementing advanced methodologies for lead generation, content optimization, and community engagement. Through these efforts, the project will drive sustainable growth, increase brand visibility, and foster stronger relationships with both customers and our team of contracted instructors.

AI

The project intends to create AI agents for the financial services and insurance, transportation & logistics and public sector verticals. This will allow us to expand the reach and adoption of AI and GenAI at the Enterprise by adding ML capabilities as part of the intelligent automation solutions we build.
The candidate will be responsible for performing requirements discovery, exploratory data analysis, model development and model deployment.
The approaches to be utilized will range from statistical modelling, time series, Natural Language Processing, Computer Vision to LLMs, depending on the specific requirements of the solution.

Required Skills :Masters or PhD students in Computing Science, Mathematics, Physics, Engineering or related fields, focusing on Data Science would be the preferred profile. Experience in development of machine learning solutions, including NLP, Computer Vision, Generative AI, Time Series and knowledge of Python. Experience with cloud environments, like Microsoft Azure, Amazon AWS or Google GCP and the associated ML/AI suites. Knowledge of Vector Databases, Retrieval Augmented Generation and Large Language Models is an asset.

Towards Accelerating the Social Tipping Points in Sustainable Behaviors: A Behavioral Segmentation of the Quebecer Population

The societal ecological transition required to address climate change must occur rapidly, to save as much as we can of the planet’s ecosystem. To do that, long-term moralization processes need to be complemented with short-term behavioral change interventions.
A key component of successful behavioral change, whether in the short or long run, is understanding the target audience, recognizing that individuals differ in their motivation, intentions, and most importantly in their readiness to change. Therefore, a segmentation exercise is primordial.
This project will conduct a segmentation through a representative survey of approximately 2,000 Quebec residents, guided by the behavioral change framework, and the long-term moralization processes mentioned above. The segmentation will be theory-driven, incorporating behavioral change theories as well as moralization theories. The analysis builds on both hierarchical and non-hierarchical clustering methods for segmentation. This approach ensures theoretical relevance, generalizability, and empirical validation.
The objectives are to develop a comprehensive guide for designing targeted interventions within Quebec, establish a theory-driven segmentation model, and create a framework bridging short-term situational changes with long-term moralization. The results will inform policy and intervention design, facilitating a faster and more effective ecological transition.

L2M – A high-sensitivity, field-portable laser-based probe for soil monitoring

We are developing a portable soil analysis system based on laser-induced breakdown spectroscopy (LIBS). During LIBS, an intense laser pulse generates plasma on the soil surface, and the emission from this plasma serves as a fingerprint of the soil’s elemental composition. The signal variability caused by the heterogeneous soils and challenges of quantifying complex composition are overcome by machine learning (ML) data modeling. This novel ML-LIBS device will significantly reduce the cost and processing time of soil analysis, supporting real-time monitoring of soil nutrients, composition, and fertilizer optimization for sustainable cultivation.

Grizzly Bear Movement Ecology, Habitat Selection, and Population Connectivity between threatened populations in southern British Columbia

This research project is focused on grizzly bear populations in southern British Columbia at the extent of their
species range. While population recovery has occurred in parts of the ragnge, some popualtions continue to deline
and large areas remain extirpated. Where the degree of population fragmentation is known this project will focus
on analyzing how bears choose their habitat, their diet, and behavioural patterns are affected by hydroelectric
development in key areas. Where the degree of population fragmentation is less known the project will use genetic
information to understand how populations are connected and what landscapes and human infrastructure help or
hinder connectivity. The ultimate aim is to give Indigenous communities the tools to lead conservation efforts and
guide grizzly bear recovery on their lands. This work is deeply important, as it respects and supports Indigenous
stewardship of the environment.

Estimation de la variance des estimateurs semiparamétriques de l’efficacité vaccinale avec le devis test-négatif : étude de simulation.

The test-negative design (TND) is an observational study design that is currently and routinely being used globally to evaluate the effectiveness of vaccines against COVID-19 illness caused by emerging SARS-CoV-2 variants. Recently, more reliable statistical estimators were proposed to get better estimates of vaccine effectiveness. However, no one has yet investigated what statistical methods will best allow us to understand the accuracy of these estimates (which is a fundamental component of statistical analysis). This project proposes to compare different ways to estimate the estimator accuracy using synthetic data generated under a hypothetical TND.