3 Mitacs Globalink (GRI) research projects for Summer 2027.
1. AI-Powered Predictive Maintenance: Smart Solutions for Energy and Industry 4.0
This research project will develop a smart framework for predictive maintenance of industrial equipment and infrastructures using multimodal data (thermal, visual, vibrational) and AI-driven analytics. The project targets sectors such as renewable energy (e.g., wind turbine blades), manufacturing (e.g., automated assembly lines), and transportation (e.g., rail or heavy vehicles).
Students will work with real or semi-real datasets captured from industrial partners and experimental setups. Tasks include data preprocessing, anomaly detection using deep learning (CNN, autoencoders), fault classification, and estimation of component degradation and RUL.
The student will also explore techniques like sensor fusion, transfer learning, and interpretable AI to improve reliability and generalization of the models. The project includes opportunities to co-author research publications and contribute to an industrial decision-support tool that enhances equipment uptime, reduces maintenance costs, and improves safety.
Research area, student roles & skills
Research area: Our research focuses on intelligent predictive maintenance of industrial equipment and large-scale structures using a combination of artificial intelligence, sensor fusion, 3D scanning, and thermographic imaging. We aim to estimate the residual useful life (RUL) of components and detect early-stage anomalies in critical assets within energy, manufacturing, and transportation sectors.
Student roles:
The student will assist in developing components of a predictive maintenance system that uses artificial intelligence to analyze equipment health across industrial settings. The student’s tasks will be adapted to their level, with an emphasis on learning and hands-on experience.
Key responsibilities include:
-Assisting in the preparation and organization of multimodal datasets (images, thermal data, vibration signals)
-Supporting basic implementation of AI models for fault detection and system health monitoring
-Conducting experiments to evaluate model performance across use cases (e.g., motors, wind turbines)
-Collaborating with team members in weekly meetings to discuss project progress
-Contributing to documentation and, if appropriate, the preparation of academic posters or reports
The student will be trained in the basics of machine learning, sensor-based data processing, and predictive maintenance concepts. They will work in a supportive, interdisciplinary environment and be mentored to develop skills in applied AI, data analysis, and industrial problem-solving.
This internship offers a unique opportunity to bridge theoretical knowledge and real-world applications while contributing to developing more innovative, safer, and more efficient industrial systems.
Skills required:
The ideal candidate has a background in an engineering discipline or applied AI. Familiarity with Python, deep learning frameworks, signal/image processing, and data analysis is essential. Experience with industrial systems or sensor data is a strong asset.
2. Machine Learning-Assisted Materials Design
I invite applicants to join a highly collaborative and interdisciplinary research team, and work closely with partners from government and industry. A range of projects are available depending on applicant interests and these span the following applications:
• Design and advanced manufacturing of next-generation lightweight materials for personal and vehicle protection
• Characterization of bio-materials (e.g., brain and bone) and development of synthetic materials.
• Design and advanced manufacturing of coatings and structures for use in conventional and clean energy applications
Students will receive structured technical training using Canadian-leading state-of-the-art experimental (e.g., ultra-high-speed cameras) and computational tools (e.g., high performance clusters). Students will also receive Professional Development training (e.g., career development, entrepreneurship) and individualized mentorship. These skills will be important for long-term careers as engineers and scientists.
Check my group’s website for opportunities for you to learn and grow: https://sites.ualberta.ca/~jdhogan/
Research area, student roles & skills
Research area: My research interests are in the microstructure-based design of materials for use as structural components in dynamic environments. Applications include defense, aerospace, bio-mechanics, and conventional and clean energy. I apply novel experimental and computational mechanics approaches to solving these problems, and enjoy developing interdisciplinary and collaborative projects.
Student roles:
In these projects, students will lead innovative and independent research projects. Students will perform experiments, modelling, and data analysis, and attend bi-weekly group meetings to present their work. Students will interact with government and industry collaborators routinely.
Skills required:
Students are encouraged to have taken relevant courses in mechanics and materials, materials science, statistics, geomechanics, rock mechanics, or related courses. Students should be comfortable with performing literature reviews, writing and presenting, and doing data analysis in Matlab or comparable software.
3. Performance Assessment of Local Wood-Derived Bio-Oils for Asphalt Binder Rejuvenation
The transportation sector faces growing challenges associated with pavement deterioration and the environmental impacts of petroleum-based construction materials. Aging of asphalt binders leads to increased stiffness and brittleness, resulting in cracking, reduced flexibility, and shorter pavement service life. The increasing use of Reclaimed Asphalt Pavement (RAP) contributes to sustainability efforts; however, the aged and oxidized binders present in RAP can negatively affect mixture workability and resistance to cracking.
Rejuvenators are widely used to restore the physicochemical and rheological properties of aged binders. While conventional petroleum-based rejuvenators are effective, they are not aligned with long-term environmental sustainability objectives. Bio-based rejuvenators derived from renewable resources have therefore emerged as promising alternatives. In particular, wood-derived bio-oils produced through pyrolysis of forestry residues offer a renewable, locally available, and environmentally responsible solution.
Quebec has abundant forestry resources and a well-established bioeconomy capable of producing bio-oils from species such as black spruce, jack pine, and balsam fir. Despite their strong potential, limited knowledge exists regarding their effectiveness in restoring aged asphalt binders. In addition, variations in bio-oil composition, depending on feedstock and processing conditions, may influence compatibility, aging resistance, and long-term pavement performance.
This project aims to evaluate the potential of locally produced wood-based bio-oils as sustainable rejuvenators. A comprehensive experimental program will assess their physical, rheological, and chemical properties to identify the most effective candidates, ultimately supporting more durable and environmentally friendly pavement systems.
Research area, student roles & skills
Research area: This research area centers on asphalt materials and pavement engineering, with a strong focus on sustainable transportation infrastructure. It combines asphalt binder rheology with advanced thermal and chemical characterization techniques to improve understanding of material behavior and aging processes. It also explores the use of bio-based materials, including renewable rejuvenators, to enhance the performance and sustainability of asphalt mixtures. In addition, this research involves pavement performance evaluation to improve durability, resistance to environmental stresses, and long-term service life, while minimizing the environmental impact of road construction and maintenance.
Student roles:
The objective of this project is to evaluate the effectiveness of Quebec-derived wood-based bio-oils as rejuvenators for aged asphalt binders.
The student will be responsible for:
Conducting a comprehensive literature review on bio-based rejuvenators for asphalt applications
Preparing aged asphalt binder samples using standard laboratory aging procedures
Incorporating different types of bio-oils at varying dosage levels into the aged binders
Evaluating conventional binder properties, including penetration, softening point, and viscosity
Performing rheological characterization using a Dynamic Shear Rheometer (DSR)
Carrying out chemical and thermal analyses using techniques such as FTIR and DSC
Analyzing and comparing the rejuvenation performance of different bio-oils
Interpreting experimental data and contributing to the development of conclusions and recommendations
Preparing technical reports, presentations, and scientific publications
This work will contribute to the advancement of sustainable, locally sourced asphalt materials, supporting circular economy principles and improving the durability and environmental performance of pavement systems.
Skills required:
The ideal candidate should have a background in Civil Engineering (preferably with coursework in Asphalt Technology and Pavement Engineering), Ceramic Engineering, or Chemical Engineering. A strong foundation in materials science is essential, along with an interest in asphalt materials and pavement performance. Knowledge of rheology, and thermal and chemical characterization techniques is considered an asset.