Projets novateurs réalisés

Advancing Soft Robotics Fabrication via Multi-Material Additive Manufacturing for Enhanced Sensing and Actuation

Whether conforming safely with the movements of humans, executing adaptable motion to navigate unpredictable terrain, or handling delicate objects with precision, soft robots are revolutionizing the way machines interact with the world. Soft robots take inspiration from biological systems, incorporating soft and stretchable materials to achieve lifelike motion. However, the fabrication of these advanced systems requires equally advanced manufacturing capabilities. This collaborative project between the University of Alberta’s IMPACT Lab and UC San Diego’s Bioinspired Robotics Lab aims to improve the way that soft robots are designed and made using next generation 3D printing techniques. By developing new methods to print soft and flexible materials, we aim to create better 3D printable pneumatic actuators, sensors, and valves. These components make up soft robotic building blocks which can be combined to design fully 3D printable soft robots capable of complex logic and tasks. The project will involve both hardware development and material optimization to enhance the performance of 3D printed soft robots. The knowledge gained from this collaboration will benefit both institutions by improving fabrication techniques, enabling new robotic applications, and strengthening international research partnerships.

Singularity formation for the hydrostatic Euler Equations

This proposal aims to integrate mathematical analysis with numerical simulations using Physics-Informed Neural Network (PINN) schemes to investigate the primitive equations, a fundamental model for atmospheric and geophysical flows. The primary focus is on achieving a precise characterization of singularity formation in these models.

Estimation of Parcel Flows on a Tram-Cargo Network

The GRAME is currently conducting an extensive opportunity study to highlight the potential role of trams in the movement of people and goods within the Greater Montreal Area.
Through this project, it has collaborated with Coop Carbone and GoMove to assess the potential for parcel distribution via a possible pan-Montreal tram network. According to modeling, a network similar to the one
proposed in Montreal’s 2050 Urban Planning and Mobility (PUM) plan, could serve up to 84% of the parcel journeys transported by Montreal distributors, using a combination of trams and cargo bikes for the final 3.5
to 5 kilometers. This indicates the coverage level for parcel delivery needs in the area by such a tram network. However, we still lack data on the capacity in terms of parcel volume transported, which is valuable information for the relevance of the study, according to our partners of the Goods Transportation Innovation Group, formed to guide the needs that the study must address. We know that this is particularly crucial for estimating the potential economic impact of this system. Additionally, it will help anticipate the capacity, primarily considering the mobility needs of users, which we believe is essential to social acceptability.

Benchmarking fundamental components of quantum machine learning on full-stack photonic quantum computers

Architectures for quantum computing can only scale effectively with suitable benchmarking techniques. Benchmarking is essential for evaluating the performance of quantum computers, including their algorithms and applications. This principle extends to quantum machine learning techniques, where benchmarking basic quantum machine learning methods on full-stack photonic NISQ devices is crucial.
A key challenge involves adapting quantum machine learning algorithms to Quandela’s technology and assessing their performance. Successful benchmarking could yield significant social and economic benefits, enabling the evaluation of quantum computing’s potential in addressing current AI challenges. These challenges include the increasing demand for data, the need for large HPC centers, and high energy consumption. This project aligns with Quandela’s mission to advance quantum technologies and drive meaningful innovation in AI and photonic quantum computing.

Machine learning developer intern(s) within cross-functional teams to develop and commercialize AI-powered solutions (17)

AltaML builds artificial intelligence (AI)-enabled solutions to business problems. We work with organisations, bringing together their data and domain expertise with our AI expertise, to develop AI solutions that are deployed in their operations. We also commercialize AI-enabled products business via
industry-specific ventures, yielding scalability from our investment in the first solution. Qilong Yu (Machine Learning Developer intern) will work closely with the Canadian Space Agency (CSA) on their GenWhales use case. The project aims to leverage Generative AI techniques and Reinforcement learning with human feedback for synthetic image generation. We identified two GenAI approaches and during this project want to experiment with both of them to improve and create usable images. These synthetic images would then be used to further train and improve Object detection and image classification models, which will in return enable the preservation of whales, improving Canadian water ways and aquatic ecosystems.

The Effects of Active Road Signs on Road Safety and Driving Behavior

This project seeks to evaluate the benefits of deploying active road signs on road safety and driving behavior. Different types of iluminated and traffic responsive traffic signs will be considered, under urban and rural setting. Two case studies will be analyzed for this purpose. The company expects to identify demonstrate that their products are beneficial to improve road safety. This represents an initial stage of the larger project. Eventually, the industrial partner seeks to develop a global framework for deployment of active traffic signs based on several factors (e.g. type of roadway, speeds, traffic volume, road users, etc.). The intern will benefit from exposure to the latest advacements in the area of traffic data collection and processing. Also, the student will be in charge with developing and executing the analysis methodology of the collected traffic data. Road safety is a very important studye in are in transportation engineering and this is also the focus area of the PhD student involved in the project.

3D printed concrete construction

This project will contribute to developing design guidelines for steel-reinforced 3D printed concrete structural members. 3D printing is a novel construction method that is being developed around the world. It has the potential to revolutionize construction practices, because it is an automated process that requires less heavy labor compared to regular construction practices. The material used for 3D printing structural members has similar properties to ordinary concrete, however there are few critical differences that make the design codes developed for reinforced concrete construction possibly not applicable for 3D printed reinforced concrete, unless modified.

This proposal will investigate one of these critical differences-the fact that the aggregate sizes need to be a small fraction of those common in reinforced concrete. This means that the development length of the steel rebar in 3D printed concrete will need to be re-established for this new type of material and construction. The development length is an important parameter that needs to be satisfied in all reinforced concrete designs, to assure that the full design capacity of the section can be developed. This proposal will determine the development length and spacing of steel rebars needed for successful 3D printed construction.

Development of Solution-Processed Zinc Oxide Nano Ink for High-Performance Organic Photovoltaics

This project, led by PINA Creation Inc. in collaboration with Simon Fraser University (SFU), focuses on developing a new zinc oxide (ZnO) nano ink for use in organic solar cells. Traditional ZnO layers require high temperatures and complex fabrication, making them expensive and difficult to scale. By creating a low-cost, solution-based ZnO ink, this project aims to improve efficiency, stability, and ease of manufacturing for next-generation solar cells. The research includes developing the ink, applying it in solar cells, and testing performance to ensure reliability. The results will help advance clean energy technology, making solar power more affordable and scalable. This work will contribute to scientific publications, industry partnerships, and commercialization efforts, positioning PINA Creation Inc. as a leader in sustainable nanomaterials.

Technical product intern(s) working within cross-functional teams to commercialize AI-powered solutions (1)

AltaML builds artificial intelligence (AI)-enabled solutions to business problems. We work with organisations, bringing together their data and domain expertise with our AI expertise, to develop AI solutions that are deployed in their operations. We also commercialize AI-enabled products business via industry-specific ventures, yielding scalability from our investment in the first solution. AltaML’s AI Lab for Government, also known as GovLab, is a talent accelerator for public service professionals, post-secondary students and recent graduates. GovLab.ai’s mission is to set a global example of how to transform the public sector through applied AI, and is designed to encourage the growth of technical and business AI skill sets that are in high demand across Alberta and around the world. AltaML’s Venture Studio is an incubator program that works with founders and co-founders in the emerging tech industry to scale ideas, build venture products, and launch AI/ML startups across numerous industries.

Développement d’un cadre d’évaluation des pôles de mobilité durable à Montréal

D’ici 2050, l’Agence de mobilité durable (l’Agence) projette de déployer 150 pôles de mobilité, en vue de promouvoir et fournir une diversité de transports alternatifs durables, améliorer l’efficacité des déplacements urbains et accroître la qualité de vie dans les quartiers. Le premier pôle de mobilité a été inauguré en 2024. Dans ce contexte, des outils de planification, de mise en œuvre et d’évaluation sont développés et affinés. Le projet de recherche proposé vise à développer avec l’Agence un cadre d’évaluation des retombées des pôles de mobilité durable sur les quartiers où ils sont implantés au regard de critères relatifs à la mobilité durable et au tissu urbain (environnement naturel et bâti) et socioéconomique. La prémisse de recherche repose sur la prise en compte du tissu urbain et socioéconomique propre à chaque secteur de la ville où les pôles de mobilité durable sont déployés, afin d’optimiser leur conception et leurs composantes.

Cartographie et classification du terrain à potentiel avalancheux au Nunavik (nord du Québec)

Les aléas naturels et les risques associés deviennent plus fréquents dans les régions arctiques et subarctiques du Canada, incluant le Nunavik. Ils peuvent avoir des impacts dévastateurs sur la sécurité, le bien-être, l’économie de subsistance, les activités et l’identité culturelle des communautés inuites. La nature et l’ampleur des événements varient selon les paramètres environnementaux et le degré d’exposition des sites. Il est donc primordial de mener des recherches collaboratives sur les aléas naturels, notamment les avalanches de neige, dont les connaissances sont fragmentaires. Actuellement, les cartes utilisées par les entrepreneurs et aménagistes sont basées sur des informations anciennes (30 ans) et non sur des études de terrain solides ni sur des échanges avec les communautés concernées. Avec les changements humains (hausse démographique) et naturels actuels, il est essentiel de refaire ces cartes en utilisant des approches méthodologiques nouvelles. Cette étude vise à identifier les facteurs déclencheurs d’avalanches pour déterminer les villages les plus à risque. Ces facteurs incluent la topographie, la géologie de surface, la végétation et les conditions météorologiques. L’objectif est de fournir à l’Administration régionale Kativik des connaissances et des simulations pour orienter l’aménagement du territoire du Nunavik.

Deep Learning-Enabled 3D Fluorescence Imaging for Cancer Surgery

The Princess Margaret Cancer Centre, part of the University Health Network (UHN), is Canada’s largest cancer hospital and one of the top 5 cancer research centres in the world. This internship will take place in the Guided Therapeutics (GTx) lab, part of the cancer centre’s research institutes. The GTx lab is developing novel devices, algorithms, and nanoparticles to guide cancer surgery. The multi-disciplinary team consists of surgeons working alongside engineers, physicists, computer scientists, and chemists. Novel technology developed in the lab translates to first-in-human clinical studies, with a strong academic focus (e.g., peer-reviewed papers, conference presentations). Cancer surgeons face the challenge of determining how deep a tumour invades below the surface. Inaccurate resections leave tumour behind and negatively affect patient outcomes. Medical devices to image the patient during surgery may help guide more precise tumour removal. To this end, the GTx lab is developing an AI-powered optical imaging system to measure tumour depth. To date, this prototype system has undergone pre-clinical testing in simplified, 3D-printed models derived from real patient cases. This internship will help the team optimize this technology to work in more realistic preclinical experiments, in preparation for future clinical studies with cancer patients.