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Engg-Systems and Technology

8 Mitacs Globalink (GRI) research projects for Summer 2027.

1. Building a simulator for optimizing windbreak systems

This research project invites students to develop an interactive simulator designed to optimize windbreak systems in agricultural and forested landscapes. By combining environmental data, basic programming, and geospatial analysis, the candidate will model how wind patterns interact with vegetation structures for limiting snow blowing. The simulator will integrate variables such as wind speed, tree density, spacing, and orientation to evaluate their impact on soil protection and crop productivity. Students will gain hands-on experience in coding, data analysis, and environmental modeling while contributing to practical, climate-resilient land management solutions. The project emphasizes creativity, problem-solving, and interdisciplinary collaboration in a real-world sustainability context.

Research area, student roles & skills

Research area: New technologies, phenocam, smart forests, global change, timings and dynamics of tree growth, phenology, biogeography, ecology

Student roles:
The candidate will take on an exciting role as both a junior researcher and simulator developer. He/she will design and implement key components of a windbreak optimization simulator by combining basic programming with environmental data analysis. the tasks will include exploring wind and landscape datasets, translating real-world processes into simple computational models, and testing different windbreak configurations. The candidate will collaborate with supervisors to refine simulation logic and visualize results using user-friendly interfaces. This role offers a unique opportunity to build technical skills while contributing to innovative, climate-smart solutions for sustainable land and forest management and improving the security of the roads in a context of snow blowing.

Skills required:
Expertise in one of the following fields spanning from environmental sciences to engineering: computer science, programming, weather, remote sensing, ecology, meteorology, quantitative data analysis. We need imagination and efficiency to find innovative solutions for data collection in the field and data analysis. The candidate should like to work both in the field and in the lab.

2. Conception et Simulation de Circuits Quantique-Photoniques pour Capteurs Intelligents

Ce projet de recherche vise à concevoir et à simuler des circuits quantique-photoniques intégrés destinés au développement de capteurs intelligents de nouvelle génération. Il s’inscrit dans un contexte où la miniaturisation, la précision et l’efficacité énergétique des systèmes de détection constituent des enjeux majeurs pour les applications industrielles, environnementales et technologiques avancées. Les travaux porteront sur la modélisation physique et numérique de dispositifs photoniques et quantiques intégrés, tels que les guides d’ondes, les interféromètres, les résonateurs et les structures hybrides. L’objectif est d’optimiser leurs performances en termes de sensibilité, de stabilité, de bruit et de robustesse face aux perturbations externes. Le projet s’appuiera sur des outils de conception et de simulation de pointe, notamment Cadence, L-Edit, Luceda IPKISS et Keysight Quantum EMpro, afin de développer des architectures innovantes et fiables. Ce travail contribuera à l’émergence de capteurs quantiques intégrés capables d’améliorer significativement les performances des systèmes intelligents.

Research area, student roles & skills

Research area: Mon domaine de recherche est axé sur le développement des dispositifs quantiques et photoniques avancés et leur intégration dans des systèmes intelligents pour la surveillance en temps réel. En combinant les principes de la physique quantique, de l’ingénierie des matériaux, et de l’Internet des Objets (IoT), mes travaux visent à concevoir des dispositifs de détection ultra-sensibles, capables de fonctionner dans des environnements complexes, notamment dans les secteurs de l’électronique de pointe et de la cybersécurité matérielle.

Student roles:
L’étudiant participera activement à la conception, à la modélisation et à la simulation de circuits quantique-photoniques intégrés pour capteurs intelligents. Il utilisera des outils technologiques de CAO avancés et des environnements Python afin d’analyser, optimiser et valider les performances des dispositifs. Il contribuera également à la rédaction de rapports techniques, à l’interprétation des résultats et au développement de solutions innovantes.

Skills required:
Le candidat idéal possède une formation en physique appliquée, génie électrique ou nanotechnologie, avec des connaissances en électroniques et/ou microélectroniques. Des compétences en programmation scientifique, modélisation et conception numérique ou analyse de données expérimentales sont souhaitées. Une expérience en conception des dispositifs quantiques-photoniques est un atout. Le candidat doit faire preuve de rigueur scientifique, de curiosité intellectuelle et être capable de travailler de manière autonome tout en collaborant au sein d’un environnement de recherche multidisciplinaire.

3. Development of smart garments with mobility enhancement function

25% of Canadians live with a condition that impacts their mobility due to muscle weakness. Worldwide, about 1.7 people experience mobility impairment. This can be the result of an injury such as a sprain, a broken bone or spinal cord injury; due to a health issue such as a stroke or a neuro-musculoskeletal disease; or associated with age, with a very large proportion of order adults experiencing various forms of mobility impairment. These mobility issues have a major impact on quality of life, mental health, independence, and financial security. They also have a huge financial impact on Society: for example in Canada, expenses associated with neuro-musculoskeletal conditions and injuries represent more than 10% of the total healthcare costs. Our project aims to contribute to providing a solution to these mobility issues by developing smart garments with mobility enhancement function. The smart garment will involve embedded sensors and actuators to provide passive and active support to the trunk, arms, and legs. With the adaptive properties built into the fibres of the fabric itself, the garments will offer adaptive mechanical properties that will augment posture, balance, strength, walking, and mobility. This project builds on the multidisciplinary expertise of 90 researchers in 43 institutions worldwide and involves several industry partners. Within the Technology and Textile Work Group, the Textile Design and Integration team activities include textile integration of smart components in collaboration with the actuator, sensor, control, and powering teams, which will be the topic of the student’s project.

Research area, student roles & skills

Research area: Smart textiles can sense changing conditions, perform actions, and adapt their performance to the environment. When integrated into garments, they allow harnessing the large contact surface area with the skin to provide a multitude of sensing and actuating functions to the garment. Current applications including monitoring vital signs and providing heat and cooling to the body. Smart garments’ potential impact in the medical field and in protection is immense, and a life changer for people benefitting from it.

Student roles:
The mission of the student in this project includes the following tasks:
• Prepare an experimental design
• Produce samples
• Characterize the sample performance
• Analyze the results
• Produce technical reports
• Prepare progress presentations

The student will work in close collaboration with the rest of the research team, which includes Master and PhD students, postdoctoral fellows, and other interns. They will be trained on different manufacturing and characterization techniques relevant to textile integration of smart components in the state-of-the-art laboratories at the University of Alberta. Throughout the internship, they will have the opportunity to interact with the researchers from different disciplines in the Textile Design and Integration team and the industrial partners involved in the project.

Skills required:
In addition to solid skills in electrical and/or textile engineering, it is critical that the student is curious and rigorous, as well as open to interdisciplinary research. They should be able to work independently while displaying a good ability for teamwork. Oral and written communication skills are important as well. Some lab work experience, for instance at preparing samples and characterizing materials performance, is an asset.

4. Integrating AI and Remote Sensing for Planning Windbreak Systems

This project explores how artificial intelligence and remote sensing can support the design of efficient windbreak systems in agricultural and forested landscapes. Interns will analyze satellite imagery and geospatial data to identify areas vulnerable to snow blowing. Using basic machine learning models and GIS tools, they will map optimal locations and configurations for windbreaks. The project combines environmental science and data analysis, offering hands-on experience in geospatial technologies. Expected outcomes include improved land management strategies that enhance climate resilience, protect soils, and support sustainable agroforestry practices.

Research area, student roles & skills

Research area: New technologies, phenocam, smart forests, global change, timings and dynamics of tree growth, phenology, biogeography, ecology

Student roles:
As a student intern, you will play an active role at the intersection of environmental science and cutting-edge technology. You will work with satellite imagery and geospatial datasets to explore landscape patterns and identify areas most vulnerable to snow blowing. Using GIS platforms and introductory AI techniques, you will contribute to building models that support smart windbreak planning. You will collaborate with researchers, interpret spatial results, and help translate data into practical land management recommendations. This role offers hands-on experience in remote sensing and AI while contributing to innovative, climate-resilient solutions for sustainable agroforestry systems.

Skills required:
Expertise in one of the following fields spanning from environmental sciences to engineering: computer science, field sensors, weather, remote sensing, ecology, meteorology, quantitative data analysis. We need imagination and efficiency to find innovative solutions for data collection in the field and data analysis. The candidate should like to work both in the field and in the lab.

5. IoT technology to build bio-sensor networking to store and manage crops

Potatoes grow in Canada once a year only in the summer season. The harvested yield is kept in storage units in big quantities in autumn, winter, and spring during which batches of potatoes are sent to customers gradually. The environment in storage needs to be controlled throughout the period to control the ventilation system and prevent disease outbreaks. Also, potatoes maybe grown in smaller quantities, during winter indoor under controlled environment. In order to development environment control, we need to measure all the parameters such temperature, humidity, CO2, moisture, light, etc. and control indoor equipment such as heaters, ventilators, light quantity etc. In this project we try to quantify all the parameters by developing reliable environmental sensors and develop two methods of control; automated based on sensor feedback, or over the air based on the internet of things concept (IoT). Our objective is to create indoor environmental conditions where potato can produce high yield by reducing the probability of disease outbreak by having maximum control on the environment.

Research area, student roles & skills

Research area: The research area is digital and precision agriculture, in which methods of sensing and automation are applied on agricultural machines and indoor plant growing areas so as to optimize agricultural production and operations. The objectives include reducing labour, reducing the usage of chemicals, optimizing the usage of seeds and fertilizers, and increasing yield. The methodologies include developing sensors to collect data from machines, equipment, and their surrounding environment, developing methods to extract information from the data, and developing autonomous systems to control machines and equipment.

Student roles:
A student working in this project is expected to contribute in installing and mounting sensors and developing communication between sensor and micro-controllers

Skills required:
Programming skills using Python
Building electronic circuits
Also any of these skills will be preferable:
Sensor sensing techniques
Handling Arduino or Raspberry Pi

6. Reliable Post Quantum Key Exchange Protocol

This project aims to design and evaluate a reliable post-quantum key exchange protocol tailored for environments with intermittent physical connectivity. Traditional key exchange protocols assume stable, continuous links; however, many real-world systems—such as satellite communications, mobile ad hoc networks, and remote IoT deployments—experience frequent disruptions, delays, and packet loss. These challenges become more complex when integrating post-quantum cryptographic primitives, which often have larger key sizes and higher computational costs. The project will explore protocol adaptations that ensure robustness against link interruptions while maintaining strong security guarantees. Key aspects include: (1) designing handshake mechanisms tolerant to message loss and reordering, (2) minimizing retransmission overhead, (3) integrating hybrid classical and post-quantum key exchange schemes, and (4) evaluating performance under realistic network conditions. The student will investigate existing PQC KEMs (e.g., lattice-based schemes) and study strategies such as session resumption, forward error correction, and state persistence across disconnections. The project also involves simulation or prototyping to assess latency, throughput, and reliability trade-offs. The outcome will be a prototype protocol and a performance/security evaluation demonstrating feasibility in intermittent network scenarios. This research contributes to the emerging need for quantum-resistant secure communication in challenging environments.

Research area, student roles & skills

Research area: My research focuses on applied cryptography and secure communication protocols for constrained and unreliable environments. In particular, I investigate post-quantum cryptography (PQC), key exchange mechanisms, and protocols resilient to intermittent connectivity, high latency, and adversarial conditions. The work integrates formal security models with practical system considerations, such as bandwidth limits, device constraints, and real-world deployment challenges. Applications include satellite links, IoT networks, disaster recovery communications, and delay-tolerant networks.

Student roles:
The student will conduct a literature review on post-quantum key exchange and communication in intermittent networks, design protocol enhancements, and implement a prototype. They will perform simulations or experiments to evaluate performance and reliability, analyze results, and contribute to a technical report or publication. Regular progress updates and collaboration with the research team are expected.

Skills required:
Students should have a background in computer science, electrical engineering, or a related field. Familiarity with basic cryptography (e.g., symmetric/asymmetric encryption, key exchange) is required. Knowledge of networking concepts (TCP/IP, latency, packet loss) is highly desirable. Experience with programming in Python, C/C++, or similar languages is expected. Exposure to Linux environments and experimental evaluation is an asset. Prior knowledge of post-quantum cryptography is a plus but not mandatory.

7. Robotic system to locate and lift rocks in agricultural fields

In some soils, planting potatoes can be inhibited by rocks either at the surface of the field or buried at certain depth into soil bed. When planting, places where rocks exist must be avoided. Also, rocks remaining inf field after plant emergence can break other machines especially harvesters. To avoid the trouble created by rocks existing in field after planting, we are developing a robotic system that locates rocks using cameras while planting and then removing these rocks after planting using a robotic arm mounted on a small farm wagon. The robotic arm would include a stereo vision to allow the arm the approach and lift the rock automatically once the rock is within its domain. The work in this project will include developing vision system and designing an arm end effector to grab and lift rocks either surfacing or buried in soil.

Research area, student roles & skills

Research area: The research area is digital and precision agriculture, in which methods of sensing and automation are applied on agricultural machines so as to automate certain farm activities and optimize agricultural production and operations. The objectives include reducing labour, reducing the usage of chemicals, optimizing the usage of seeds and fertilizers, and increasing yield. The methodologies include developing sensors to collect data from machines, equipment, and their surrounding environment, developing methods to extract information from the data, and developing autonomous systems to control machines and equipment.

Student roles:
A student working in this project is expected to contribute in installing and mounting machine vision systems, develop communication between machine vision and computer, draw mechanical components using AutoCAD or similar software, and conduct experiments. The student is expected to work closely with graduate students and should have good communication skills to work in a team.

Skills required:
Programming skills using one of the following languages: C++, C#, or Python
Machinery and mechanical design
Machine communication
Ability and willingness to spend time in field conducting tests

8. Tracking forest health from space with remote sensing

During insect outbreaks, authorities adopt management plans that include salvage logging to guide harvesting activities in stands affected by mortality. Unfortunately, salvage cuts are often planned and executed when most of the trees are already in an advanced state of decline or have already died. Such delays directly impact the profitability of timber harvesting. This project aims to identify patterns that integrate tree physiology and remote sensing to characterize the health status of boreal stands during insect outbreaks, with the goal of optimizing harvest planning as soon as the first signs of decline appear, before the degradation of wood quality begins. We will use radar imagery to: (1) quantify the phenology and health status of trees, (2) isolate signals associated with vegetation and soil moisture content in the stands, and (3) adapt and validate a functional model describing water exchange to predict tree mortality. We will determine algorithms to produce high spatial and temporal resolution maps of decline and mortality in a commercial boreal forest context. The application will allow public managers and companies to assess different timber recovery strategies under insect outbreaks based on site accessibility and the extent of forest decline. Our decision-making tool will facilitate the planning of salvage cuts, encourage early harvesting of affected stands, and enhance the value of the forest resource by reducing losses due to a decline in wood quality. Optimizing recovery after disturbances will allow companies to improve the profitability of logging by harvesting trees that better meet the standards required by both national and international markets.

Research area, student roles & skills

Research area: New technologies, phenocam, smart forests, global change, timings and dynamics of tree growth, phenology, biogeography, ecology, radar. for more information, you can visit the lab page at https://portfolio.uqac.ca/sergiorossi/

Student roles:
As a student intern, you will play an active role at the intersection of environmental science and cutting-edge technology. You will work with satellite imagery and geospatial datasets to explore landscape patterns and identify areas most vulnerable to snow blowing. Using GIS platforms and introductory AI techniques, you will contribute to describe trends and relationships between variables. You will collaborate with researchers, interpret spatial results, and help translate data into practical land management recommendations. This role offers hands-on experience in remote sensing and AI while contributing to innovative solutions for a healthy and sustainable forest production.

Skills required:
Students should have a background in environmental science, forestry, ecology, or geospatial sciences. Key skills include experience with remote sensing (especially radar/SAR), data analysis (using tools like Python, R, or MATLAB), and GIS software (e.g., QGIS, ArcGIS). Knowledge of forest health monitoring, tree physiology, and hydrological modeling is also valuable. Strong research skills, critical thinking, and the ability to communicate findings effectively are essential. Experience in fieldwork and collaborative teamwork will help students contribute meaningfully to the project’s goals.