Agriculture

Research area: Narendra Malalgoda is an Assistant Professor of Supply Chain Management at the Asper School of Business, University of Manitoba. He completed his Ph.D. in Transportation and Logistics, with emphasis on Logistics and Supply Chain Systems, in 2020 and holds an MSc in International Agribusiness, both from the North Dakota State University, USA. Before joining the Asper School of Business, Dr. Malalgoda completed his post-doctoral training in the Department of Agribusiness and Agricultural Economics at the UofM. Dr. Malalgoda is the Associates fellow in Supply Chain Management 2024-2027.

Student roles:
The student's primary role is to design and build a prototype digital twin of grocery store accessibility for a defined pilot region, turning the program's static datasets and analyses into a dynamic, scenario-capable model.

Early in the term, the student will work with the supervisor to scope the pilot region and define the questions the digital twin should answer, such as the effects of store openings and closures, optimal siting of new stores, or the impact of seasonal road access. They will then assemble the necessary inputs from the parallel streams: the cleaned grocery store database, the GIS accessibility layers, and supporting data such as road networks and population.

The core technical work involves building a model in which stores, networks, and populations are represented as connected, updatable components. The student will implement methods to recompute accessibility metrics, such as travel distance or time to the nearest grocery store, when inputs change, so scenarios can be run and compared. They will develop a set of example scenarios and, where time allows, a simple interactive dashboard or visualization layer so results are easy to explore and communicate.

Throughout, the student will document the model's data sources, assumptions, and limitations, and think critically about where simulated results should and should not be trusted. They will collaborate closely with the interns responsible for the database and GIS analysis, giving feedback on data structure and keeping the twin consistent with their work.
Through regular check-ins, the student will report progress, demonstrate scenarios, and refine priorities. By the end of the term, they will deliver a working prototype, supporting documentation, and a short report illustrating its use through example scenarios.

This role offers hands-on experience in geospatial modelling, simulation, and decision-support tool development.

Skills required:
The student should have basic programming experience, preferably in Python (libraries such as GeoPandas or NetworkX are an asset). Familiarity with GIS concepts and tools (QGIS or ArcGIS) and comfort working with spatial and tabular data are expected. Some exposure to data visualization or dashboard tools is helpful. An interest in modelling, simulation, or "what-if" analysis is valuable. The student should be able to document assumptions clearly and think critically about model limitations. A background in geography, computer science, engineering, data science, or a related field is welcome. Curiosity and problem-solving matter more than advanced expertise.

Research area: Dr. Aitazaz A. Farooque specializes in precision agriculture, climate-smart agricultural technologies, remote sensing, GIS, artificial intelligence, agricultural automation, and environmental sustainability. His research focuses on developing advanced geospatial and sensing technologies for sustainable agriculture, crop monitoring, water management, climate adaptation, and digital agriculture applications. His work integrates AI, machine learning, computer vision, GPS-GIS systems, and precision farming technologies to improve agricultural productivity and environmental management. Dr. Farooque leads several nationally and internationally recognized research initiatives at the University of Prince Edward Island and the Canadian Centre for Climate Change and Adaptation. More information: https://theatlaslab.ca/

Student roles:
The student will assist in developing an AI and machine vision system for detecting crop disease, weeds, and pest-related stress using drone imagery and geospatial technologies. The student will support data collection, image organization, annotation, preprocessing, and georeferencing of RGB, multispectral, or thermal imagery.

The student will develop and test computer vision and deep learning models for identifying disease symptoms, weed patches, or stress-affected crop areas. This may include preparing training datasets, applying image augmentation, training CNN or YOLO-based models, evaluating model accuracy, and improving model performance through iterative testing. The student will also work on converting AI model outputs into GIS-ready spatial layers. This will involve mapping detected stress zones, estimating severity levels, and creating field-scale visualizations that can support targeted crop management decisions. Another key responsibility will be contributing to the development of a prototype dashboard or web GIS tool where users can view uploaded imagery, detected stress areas, and model-generated maps. The student may also assist with integrating AI outputs into interactive maps, reports, and decision-support workflows.

The role will provide practical experience in machine vision, drone-based remote sensing, geospatial AI, precision crop protection, and agricultural automation. The student will be expected to document methods, support validation using field observations, prepare figures and maps, and contribute to technical reports or research manuscripts.

Skills required:
The ideal student should have a background in Computer Science, Engineering, GIS, Remote Sensing, Agricultural Engineering, Plant Science, or related disciplines. Experience with Python, OpenCV, TensorFlow/PyTorch, YOLO, image processing, drone imagery, GIS software, and web development would be an asset. Familiarity with crop disease symptoms, weed detection, pest monitoring, georeferenced imagery, and precision spraying concepts would be beneficial. The student should be motivated to work at the intersection of AI, agriculture, robotics, and geospatial technology.

Research area: Dr. Aitazaz A. Farooque specializes in precision agriculture, climate-smart agricultural technologies, remote sensing, GIS, artificial intelligence, agricultural automation, and environmental sustainability. His research focuses on developing advanced geospatial and sensing technologies for sustainable agriculture, crop monitoring, water management, climate adaptation, and digital agriculture applications. His work integrates AI, machine learning, computer vision, GPS-GIS systems, and precision farming technologies to improve agricultural productivity and environmental management. Dr. Farooque leads several nationally and internationally recognized research initiatives at the University of Prince Edward Island and the Canadian Centre for Climate Change and Adaptation. More information about ongoing research: https://theatlaslab.ca/

Student roles:
The student will contribute to the development and implementation of an automated agricultural drought monitoring platform using Remote Sensing, GIS, Artificial Intelligence (AI), and Machine Learning (ML) technologies. The role will involve collecting, processing, analyzing, and visualizing geospatial and climate datasets to support drought assessment and precision agriculture applications.

The student will work with multi-source satellite imagery and geospatial datasets including vegetation indices, climate variables, precipitation data, land surface temperature, and soil moisture datasets. Responsibilities will include preprocessing satellite imagery, calculating drought-related indices such as NDVI, NDWI, VCI, and TCI, and performing spatial and temporal analysis using GIS and remote sensing techniques. The student will also support the development of automated geospatial workflows and web-based GIS applications for interactive drought visualization and monitoring. This may include integration of Google Earth Engine, Python-based geospatial libraries, spatial databases, and web GIS frameworks for real-time or near real-time drought analytics. Additionally, the student will assist in implementing and evaluating machine learning and AI models for drought classification, prediction, and decision-support applications. The role may involve testing different modeling approaches, validating outputs, generating maps and dashboards, and contributing to technical documentation and research publications.

The student will collaborate within a multidisciplinary research environment involving precision agriculture, climate-smart technologies, and geospatial innovation. The position provides an opportunity to gain experience in applied research, geospatial AI, cloud-based spatial analytics, and digital agriculture systems while contributing to impactful solutions for agricultural sustainability and climate resilience. Strong communication, teamwork, and problem-solving skills are expected, along with the ability to work independently on assigned research and development tasks.

Skills required:
The ideal student should have a background in GIS, Remote Sensing, Geomatics, Computer Science, Engineering, Environmental Science, or related disciplines. Experience with agriculture, geospatial technologies, satellite image processing, and spatial analysis is preferred. Familiarity with Python programming, GIS software (ArcGIS/QGIS), Google Earth Engine, web GIS development, and machine learning techniques would be considered an asset. Knowledge of remote sensing indices, climate data analysis, and geospatial data visualization is desirable. The student should possess strong analytical, problem-solving, and communication skills and demonstrate interest in climate-smart agriculture, AI applications, and geospatial technology development.

Research area: Dr. Aitazaz A. Farooque specializes in precision agriculture, climate-smart agricultural technologies, remote sensing, GIS, artificial intelligence, agricultural automation, and environmental sustainability. His research focuses on developing advanced geospatial and sensing technologies for sustainable agriculture, crop monitoring, water management, climate adaptation, and digital agriculture applications. His work integrates AI, machine learning, computer vision, GPS-GIS systems, and precision farming technologies to improve agricultural productivity and environmental management. Dr. Farooque leads several nationally and internationally recognized research initiatives at the University of Prince Edward Island and the Canadian Centre for Climate Change and Adaptation. More information: https://theatlaslab.ca/

Student roles:
The student will support the development of an AI-based system for potato quality assessment and precision harvesting applications. They will assist in collecting, organizing, and annotating image datasets of potato tubers under different quality conditions. The student will work on image preprocessing, feature extraction, model training, and accuracy evaluation for detecting size, shape, defects, bruising, or disease-related symptoms.

The student will also help develop a prototype interface or analytical workflow that allows users to upload images, run AI-based quality assessment, and view classification results. They may compare different computer vision or deep learning models to determine which approaches are most accurate and practical for potato quality evaluation.

The role will include model testing, validation, documentation, preparation of visual outputs, and contribution to technical reports or research publications. This project will provide hands-on experience in machine vision, AI model development, agricultural automation, and digital tools for potato production and post-harvest management.

Skills required:
The ideal student should have a background in computer science, artificial intelligence, agricultural engineering, biosystems engineering, food systems, crop science, or a related discipline. Experience with Python, OpenCV, TensorFlow, PyTorch, image processing, machine learning, data annotation, and model evaluation would be valuable. Knowledge of potato production, tuber quality, post-harvest handling, grading systems, and agricultural automation would be an asset. The student should have strong programming, analytical, and problem-solving skills.

Research area: Dr. Aitazaz A. Farooque specializes in precision agriculture, climate-smart agricultural technologies, remote sensing, GIS, artificial intelligence, agricultural automation, and environmental sustainability. His research focuses on developing advanced geospatial and sensing technologies for sustainable agriculture, crop monitoring, water management, climate adaptation, and digital agriculture applications. His work integrates AI, machine learning, computer vision, GPS-GIS systems, and precision farming technologies to improve agricultural productivity and environmental management. Dr. Farooque leads several nationally and internationally recognized research initiatives at the University of Prince Edward Island and the Canadian Centre for Climate Change and Adaptation. More information: https://theatlaslab.ca/

Student roles:
The student will support the development of an AI-based nutrient management platform by collecting, organizing, and analyzing soil, crop, yield, and management datasets. They will assist in preparing data for machine learning analysis, identifying key variables affecting nutrient response, and developing predictive models for fertilizer recommendation and management zone identification. The student will also help evaluate model performance and compare different AI or statistical approaches for nutrient decision-making.

The student will contribute to the development of a user-friendly digital dashboard or decision-support interface that presents fertilizer recommendations, nutrient variability patterns, field summaries, and management insights. If spatial data are available, the student may also support the generation of prescription maps or zone-based nutrient recommendations for variable rate application.

The role will include data cleaning, model development, result validation, visualization, technical documentation, and preparation of reports or research outputs. Through this project, the student will gain practical experience in precision nutrient management, AI modeling, agricultural data analytics, and digital tool development for sustainable farming systems.

Skills required:
The student should have a background in agriculture, soil science, agricultural engineering, environmental science, data science, GIS, computer science, or a related field. Experience with Python, machine learning, soil data analysis, statistics, spatial analysis, and dashboard development would be an asset. Knowledge of nutrient management, crop production, variable rate technology, fertilizer recommendations, and precision agriculture would be beneficial. The student should have strong analytical, data management, programming, and communication skills, with interest in developing practical digital tools for sustainable agriculture.

Research area: Dr. Aitazaz A. Farooque specializes in precision agriculture, climate-smart agricultural technologies, remote sensing, GIS, artificial intelligence, agricultural automation, and environmental sustainability. His research focuses on developing advanced geospatial and sensing technologies for sustainable agriculture, crop monitoring, water management, climate adaptation, and digital agriculture applications. His work integrates AI, machine learning, computer vision, GPS-GIS systems, and precision farming technologies to improve agricultural productivity and environmental management. Dr. Farooque leads several nationally and internationally recognized research initiatives at the University of Prince Edward Island and the Canadian Centre for Climate Change and Adaptation. More information: https://theatlaslab.ca/

Student roles:
The student will contribute to the development and implementation of an AI-driven geospatial platform for crop yield prediction, crop health monitoring, and agricultural stress detection using Remote Sensing, GIS, Artificial Intelligence (AI), and Machine Learning (ML) technologies. The role will involve collecting, processing, analyzing, and visualizing multi-source geospatial and agricultural datasets to support precision agriculture applications.

The student will work with satellite imagery, vegetation indices, climate datasets, soil information, and crop growth parameters to assess crop conditions and agricultural variability. Responsibilities will include preprocessing remote sensing imagery, calculating vegetation and stress indices such as NDVI, NDWI, SAVI, EVI, and thermal-based indicators, and conducting spatial and temporal analysis using GIS and remote sensing techniques. The student will also assist in developing automated geospatial workflows and web-based GIS applications for crop analytics, interactive mapping, and real-time agricultural monitoring. This may include integration of Google Earth Engine, Python-based geospatial libraries, spatial databases, cloud-based processing systems, and web GIS frameworks for scalable agricultural analysis. Additionally, the student will support the implementation and evaluation of machine learning and AI models for crop classification, stress detection, crop health assessment, and yield prediction. The role may involve training AI models, validating outputs, generating geospatial visualizations and dashboards, and contributing to technical documentation, reports, and research publications.

The student will collaborate within a multidisciplinary research environment involving precision agriculture, geospatial intelligence, climate-smart farming, and digital agriculture technologies. This position provides valuable experience in applied research, geospatial AI, cloud computing, agricultural analytics, and web GIS system development while contributing to innovative solutions for sustainable agriculture and food security.

Skills required:
The ideal student should have a background in GIS, Remote Sensing, Geomatics, Computer Science, Agricultural Engineering, Environmental Science, or related disciplines. Experience with satellite image processing, spatial analysis, and geospatial technologies is preferred. Familiarity with Python programming, GIS software (ArcGIS/QGIS), Google Earth Engine, machine learning, and web GIS development would be considered an asset. Knowledge of vegetation indices, crop monitoring techniques, climate data analysis, and AI-based spatial modeling is desirable. The student should possess strong analytical, programming, problem-solving, and communication skills with an interest in precision agriculture and digital agriculture technologies.

Research area: Dr. Aitazaz A. Farooque specializes in precision agriculture, climate-smart agricultural technologies, remote sensing, GIS, artificial intelligence, agricultural automation, and environmental sustainability. His research focuses on developing advanced geospatial and sensing technologies for sustainable agriculture, crop monitoring, water management, climate adaptation, and digital agriculture applications. His work integrates AI, machine learning, computer vision, GPS-GIS systems, and precision farming technologies to improve agricultural productivity and environmental management. Dr. Farooque leads several nationally and internationally recognized research initiatives at the University of Prince Edward Island and the Canadian Centre for Climate Change and Adaptation. More information: https://theatlaslab.ca/

Student roles:
The student will contribute to the development of an AI-enabled platform for soil health, biochar, and carbon sequestration analysis. They will collect, organize, and process datasets related to soil properties, crop management, biochar treatments, environmental conditions, and soil health indicators. The student will help identify important variables that influence soil quality, carbon storage, and management outcomes.

The student will develop and evaluate machine learning models for predicting soil health indicators, estimating carbon-related patterns, or identifying suitable conditions for biochar application. They will also assist in creating visual outputs, dashboards, or decision-support summaries that communicate results clearly to researchers, producers, and agricultural stakeholders.

The role may include preparing charts, maps where relevant, scenario comparison tools, technical documentation, and research reports. Through this project, the student will gain valuable experience in AI-based environmental analytics, soil health assessment, climate-smart agriculture, and digital sustainability tools.

Skills required:
The student should have a background in soil science, agriculture, environmental science, data science, computer science, agricultural engineering, or a related field. Experience with Python, machine learning, statistical analysis, soil datasets, environmental modeling, and data visualization would be valuable. Knowledge of soil health indicators, biochar, carbon sequestration, greenhouse gas emissions, sustainable agriculture, and climate-smart practices would be an asset. The student should have strong analytical, data interpretation, communication, and problem-solving skills.

Research area: Prof. Matta Kaur’s research group uses computational biophysics, molecular modelling, and data-driven analysis to study photosynthesis, plant health, and sustainable technologies. Her group examines how light, water, protein structure, and environmental conditions influence biological function. For this project, the focus is on connecting greenhouse conditions with crop-health indicators using machine-learning and statistical methods. Research areas: Computational Biophysics, Photosynthesis, Plant-health data analysis, Greenhouse crop monitoring, Precision Agriculture, and Sustainable Agriculture. Research sub-fields: Light Quality, Plant Response, Vegetation Indices, Chlorophyll-related measurements, Environmental variables, Regression/Classification models, Clustering, Feature-importance analysis, and Data Visualization.

Student roles:
The research plan of this project is multidisciplinary, which allows the student to be integrated into different tasks related to greenhouse agriculture, plant-health monitoring, and machine-learning-based data analysis. This will offer the student exposure to several possible future career paths, including agricultural technology, plant science, environmental data analysis, computational biology, and applied AI.

The student will join Prof. Matta Kaur’s research group, where the project will be organized into specific activities: (1) literature review and identification of useful greenhouse crop-health variables; (2) data collection and organization from public or literature-reported sources, including light quality, temperature, humidity, crop type, biomass, chlorophyll-related measurements, vegetation indices, and growth conditions; (3) data cleaning, visualization, and application of machine-learning or statistical methods such as regression, classification, clustering, principal component analysis, correlation analysis, and feature-importance analysis; and (4) preparation of figures, summary tables, a short research report, and an oral presentation.

In the beginning, the student will be supervised closely while learning how to read selected papers, extract reliable information, organize data in a consistent format, and understand the biological meaning of crop-health indicators. In the second part of the internship, the student will develop skills and independence in one or more specific sub-areas according to their background and interest, such as dataset building, image or vegetation-index interpretation, statistical analysis, or machine-learning model comparison. A fundamental part of the internship will be devoted to training the student in responsible data handling, reproducible analysis, clear visualization, and scientific communication.

Skills required:
The required background for the student is in Agriculture, Biological Sciences, Environmental Science, Computer Science, Statistics, or related areas. Skills in data collection, data organization, Excel, Python, statistics, machine learning, image analysis, and data visualization will help the student understand the steps of greenhouse crop-data preparation and crop-health analysis. Basic courses in plant biology, greenhouse production, environmental science, or data analysis will be useful. Background in plant-health monitoring and AI-based analysis will be more specific to the project related to crop, light, environmental, and growth-data interpretation.

Research area: I study the reaction between water, rocks, and gases on Earth and Mars using laboratory experiments, field measurements, and numerical models.

Student roles:
The student would examine rates of carbon uptake following rock addition to croplands.

Skills required:
The student would need a basic understanding of chemistry and a willingness to work in the laboratory, performing experiments

Research area: My expertise is Bioinformatics and Machine Learning. My current research focuses on multi-omics analysis which provides novel information on the mechanisms of the biological process and cell states in disease development. We develop computational tools for complex and high-dimensional data including genome-wide population data, RNA-seq, and tandem mass spectra. I'm particularly interested in applying deep learning and AI to plants to advance digital agriculture.

Student roles:
- Test out other prediction model on our dataset
- Set up AlphaFold2 and AlphaFold3 to run the followup experiments and compare the performance.
- Test the model sysmatically on the SKEMPI dataset and evaluate the correlation.
- Combine AlphaFold for Kd prediction using more advanced framework, especially for protein-protein mutations.
- Develop detailed documentation and the user manual of the tool and publish the tool to the online repository, e.g. GitHub
- Write a final report summarizing the project

Skills required:
The student is expected to have solid programming skills using Python. Experience of High performance computing and Linux system are essential. Knowledge of R and C are preferred but not required. Good communication skills and writing skills. Knowledge of genomics is an asset but not required for this project.

Research area: I specialize in the sustainable intensification of horticultural production systems, with a focus on field-scale solutions. My research program takes a pragmatic, multidisciplinary approach to improving labor and input use efficiency, exploring practices such as amendments, crop rotations, cover cropping, and reduced tillage as entry points for long-term systems planning. I am interested in how adapting and combining these practices in ways that are practical for farmers can reduce production costs and environmental impact without sacrificing yield or quality.

Student roles:
The intern will contribute to the second year of a multi-year field experiment evaluating crop rotation and cover cropping strategies for reduced tillage in diversified horticultural systems. The student will be involved in fieldwork, laboratory analysis, and data management and analysis.

In the field, the intern will participate in sample collection at cover crop termination and in the establishment of the year 2 commercial crop under reduced or no-till conditions. The student will conduct regular crop monitoring, including plant growth and development characterization, yield and quality assessment, and pest and disease scouting. Soil physical properties will be sampled and measured at the beginning and end of the cropping cycle. In the lab, the intern will process soil and plant samples according to established protocols. In the office, the student will be responsible for data entry, organization, and basic statistical analysis of results collected throughout the season. The intern will be expected to engage in the discussion and interpretation of preliminary results.

The student will work under the supervision of the principal investigator and will be guided through key experimental steps, including sampling protocols and data analysis. Autonomy is expected for routine monitoring and data collection tasks. The intern will interact regularly with the research team and farm collaborators.

Skills required:
The project requires a student enrolled in horticulture, crop science, agronomy, or agroecology programs, with a genuine interest in sustainable production systems. Prior exposure to vegetable or horticultural production and familiarity with cover cropping practices are strong assets. Key competencies include field data collection, crop scouting, and plant growth and yield characterization. Training in soil physical properties determination protocols and basic statistical data analysis is an asset. Autonomy, rigor, organization, and adaptability to field and lab conditions are essential personal qualities.

Research area: 1) To develop productive and sustainable cropping systems in boreal climate using innovative approaches/management practices, 2) Integrated nutrient management practices to enhance nutrient use efficiency, improve soil quality and health, 3) Abiotic stress management strategies to induce stress tolerance in crop plants, 4) Developing and evaluating beneficial management practices (BMPs) (crop rotation, intercropping, cover crops, biochar amendment, nitrification inhibitors) to sequester C, improve soil health and mitigate N losses (leaching, runoff and gaseous), 5) To develop and test growth media formulations using industry waste (paper, mining, dairy and fish), 6) Synthesis, characterization and evaluation of marine waste derived carbon nano-fertilizers.

Student roles:
Measurement of greenhouse gases and soil sampling to measure N dynamics in cropping systems. Determining the area scale and yield scale global warming potential. He will collect additional parameters for the soil and plants, including Soil nitrate and ammonium analyses with an Opportunity to learn Lachat QuickChem 8500 Series. Total C & total N determination on the CHNS analyzer and DNA extraction for N-cycling genes study from soil samples. Organizing the data and statistical analyses

Skills required:
The student must be enrolled in a B.Sc (Hons.) Agriculture with a major in Soil Science, Agronomy, Environmental Science or Climate Change. This position requires sample collection experience of GHGs using the static chamber method or using the portable gas analyzer Gasmet DX-4015, and soil sampling to determine Mineral N analysis, N cycling genes & enzymes, along with other soil properties.

Research area: My research focuses on peatlands, a type of wetland that has organic, carbon rich soils. I am interested in how much carbon is stored in these soils, greenhouse gas exchange in peatland ecosystems, and how disturbance alters this storage and exchange. As the amount of carbon store is driven by site hydrology, chemistry and plant communities, many of our projects also investigate these aspects of the ecosystem. We also investigate management strategies to mitigate greenhouse gas emissions that arise from peatland disturbance, including ecological restoration.

Student roles:
The student will support all field measurements including GHG flux measurements and monitoring hydrological, physical, chemical and plant community conditions around the sampling sites. Some measurements will involve collection of soil or plant samples followed by analysis in the lab and the intern will take a leading role in processing these samples. The intern will also participate in data entry and preliminary analysis. There is an opportunity to identify a specific research question within the scope of the project for the intern to tackle as an independent project. This will be identified at the field site together with the research team and the intern will then take the lead on collecting data needed to complete this project.

Skills required:
Successful students should have background knowledge of carbon cycling, hydrology, ecology and soil science. Previous experience measuring GHG fluxes is not required as all training will be provided. Those with an interest in environmental science and the interconnections between hydrology, soil development and plant communities will enjoy this project. This project involves long days working outside, including in inclement weather, and carrying equipment into field sites, often for a few kilometers. Confidence working (or spending a lot of time) outdoors is an asset.

Research area: Our lab focuses on food chemistry and environmental research, from understanding the molecular interactions responsible for macroscopic properties to soil analysis and environmental remediation and mitigation. I work closely with the City of Orillia Waste Division to aid in the mitigation of waste-related issues.

Student roles:
The student will conduct a focused field- and lab-based investigation of microplastic and microfibre contamination associated with the City of Orillia’s wastewater treatment system. They will collect a limited number of samples from treated effluent, lagoon biosolids, and sediments in areas influenced by discharge, following established sampling protocols.
In the laboratory, the student will process samples using filtration and density separation techniques to isolate microplastics and microfibres. They will perform quantitative assessments of particle and fibre abundance using controlled visual sorting methods and classify materials into broad categories such as fragments and fibres.
The student will also use ATR-FTIR spectroscopy to confirm the polymer identity of a subset of representative particles and fibres, providing validation of visual classification and reducing uncertainty in overall abundance estimates. They will compare distributions across sample types to identify patterns of accumulation and transport within the wastewater system and receiving environment.
A final report will present methods, results, and key limitations, including discussion of uncertainty in visual identification versus spectroscopic confirmation. Lab work will take place at Lakehead University with regular check-ins with City staff and the faculty supervisor for guidance and project oversight.

Skills required:
This project is open to undergraduate students (with at least three years completed) in Chemistry, Environmental Science, Biology, or related disciplines. Basic laboratory experience is required, including careful sample handling, filtration, and quantitative lab work. Familiarity with spectroscopy (especially FTIR) is an asset but not required.
The ideal student is detail-oriented, methodical, and comfortable working with small sample masses and contamination-sensitive workflows. Critical thinking, strong organizational skills and accurate data recording are essential. Basic data analysis skills (e.g., Excel) and strong scientific writing ability are required.

Research area: Forest ecosystems. Assisted migration. biodiversity. Tree growth. Phenology. Adaptation to climate change. Biogeography. Statistical and mathematical modelling

Student roles:
The candidate will collect observations of tree survival, growth and phenology in common gardens of a number of species (in the field), and test differences among provenances using statistics and modelling (in the lab). Given the innovative nature of the work, all tasks will be performed by working with the supervisor and the other lab members.
It is an excellent opportunity to visit the wide and fascinating forest ecosystems of Canada

Skills required:
No particular expertise is required, but the task needs imagination and efficiency for the planification activity. Some ability in data analysis will be also required.
The candidate should like to work both in the field and in the lab.

Research area: Professor Jitendra Paliwal is the Vice-President, Research and Innovation at the University of Winnipeg. His internationally recognized research program at the University of Manitoba, where he holds an Adjunct Professor appointment, specializes in the post-harvest preservation, handling, and quality assessment of cereal grains, oilseeds, and leguminous crops. With over 285 peer-reviewed publications, $14 million in research funding, and an h-index of 49, Dr. Paliwal’s research utilizes electromagnetic imaging, digital twins, vibrational spectroscopy (Raman and FTIR), and machine vision. His lab aims to optimize quality monitoring and processing through spectral fingerprinting and microstructural analysis.

Student roles:
As a student researcher on this project, you will take on several key responsibilities integral to building a foundational hyperspectral imaging dataset and evaluating YOLO-based deep learning for automated yellow pea grading under CGC standards. You will begin with a structured literature review covering hyperspectral imaging of pulse and grain crops, YOLO-family object detection architectures, classical chemometric classifiers including PLS-DA and SVM, and the agronomic and commercial context of Canadian yellow pea grading. This foundation will inform imaging protocol decisions, annotation strategy, model architecture choices, and interpretation of results. You will actively participate in sample preparation, sourcing and sorting yellow pea kernels across five defect classes (Sound, Bleached, Green/Immature, Wrinkled, and Disease-damaged) in coordination with a certified CGC grain inspector. You will operate the Specim FX10 push-broom hyperspectral camera, apply white reference and dark current corrections, and maintain meticulous documentation of sample metadata including defect class, lot origin, and acquisition conditions to ensure a reproducible and well-characterized dataset.
You will take a leading role in dataset construction, performing first-round bounding box annotation on the Roboflow platform using YOLO-format labels, computing inter-annotator Cohen's kappa statistics, and implementing image augmentation pipelines to expand the dataset to at least 2,000 annotated instances. On the modelling side, you will adapt YOLOv8 for multi-channel hyperspectral input, train and validate both nano and small model variants alongside the RGB baseline, and implement PLS-DA and SVM-RBF classical classifiers for benchmarking. Model performance will be evaluated using mAP@0.5, precision, recall, F1-score, and McNemar's test. You will maintain detailed laboratory records, ensure compliance with safety protocols, and troubleshoot instrumentation and modelling challenges as they arise. Regular communication with the principal investigator and collaborators will be expected. You will contribute actively to manuscript preparation, including methodology, model performance reporting, and implications for scalable in-line grain grading systems.

Skills required:
The ideal student should possess a strong background in computer science, food science, agriculture, biosystems engineering, chemistry, or a related field. Familiarity with spectroscopic techniques, particularly infrared spectroscopy, and experience with pulse crop quality assessment would be an asset. Knowledge of chemometric or machine learning methods, including regression and classification modelling, as well as spectral preprocessing, is desirable. Proficiency in Python for data analysis and statistical computing is expected. Excellent analytical and problem-solving skills, including the ability to interpret complex spectral datasets, are essential. Effective verbal and written communication skills, along with the ability to work collaboratively is required.

Research area: Dr. Aitazaz A. Farooque specializes in precision agriculture, climate-smart agricultural technologies, remote sensing, GIS, artificial intelligence, agricultural automation, and environmental sustainability. His research focuses on developing advanced geospatial and sensing technologies for sustainable agriculture, crop monitoring, water management, climate adaptation, and digital agriculture applications. His work integrates AI, machine learning, computer vision, GPS-GIS systems, and precision farming technologies to improve agricultural productivity and environmental management. Dr. Farooque leads several nationally and internationally recognized research initiatives at the University of Prince Edward Island and the Canadian Centre for Climate Change and Adaptation. More information: https://theatlaslab.ca/

Student roles:
The student will assist in developing and testing an AI-supported precision agrochemical application system. They will support the collection and organization of image or sensor datasets related to weeds, crop stress, disease symptoms, or target application zones. The student will work on preprocessing images, training AI or computer vision models, evaluating detection accuracy, and improving model performance for field-relevant conditions.

The student will also contribute to system integration by supporting sensor setup, control logic development, and prototype testing for targeted application. This may include linking AI detection outputs with decision rules for spot spraying or site-specific treatment. The student may assist in developing a simple user interface or visualization tool to display detected targets, application decisions, and system performance.

The role will involve field or lab testing, documentation, preparation of technical reports, and collaboration with researchers working in robotics, automation, precision agriculture, and crop protection. This project will provide strong hands-on experience in AI, machine vision, robotics, and sustainable agricultural technology development.

Skills required:
The ideal student should have a background in robotics, mechatronics, computer science, engineering, agricultural engineering, artificial intelligence, or a related discipline. Experience with Python, OpenCV, machine learning, computer vision, sensors, control systems, embedded systems, or agricultural machinery would be highly valuable. Familiarity with crop protection, weed detection, precision spraying, automation, and field equipment would be an asset. The student should be comfortable working with hardware, software, data collection, troubleshooting, and applied research in agricultural environments.

Research area: My research program investigates integrated forage and grazing systems to improve the productivity, resilience, and sustainability of livestock agriculture. I study forage agronomy, plant–animal interactions, nutrient dynamics, biological nitrogen fixation, pasture biodiversity, and grazing management practices that optimize forage utilization, animal performance, and ecosystem services across diverse production environments.

Student roles:
The student will assist with field and laboratory research activities related to forage and pasture systems. Responsibilities will include collecting plant and soil samples, assessing botanical composition and forage productivity, wildlife recording, organizing data, and supporting sample processing and analysis. The student will participate in data management, preliminary statistical analyses, and interpretation of results under supervision. They will work closely with graduate students and research staff, gain experience in experimental design and scientific methods, and contribute to research aimed at improving the productivity and sustainability of forage-based livestock production systems in Saskatchewan.

Skills required:
The ideal candidate is an undergraduate student pursuing studies in Agronomy, Biology, Plant Science, Animal Science, or a related field. Students should have a strong interest in forage and pasture research, sustainable livestock production, and agricultural systems. Prior experience with field research, plant identification, data collection, or laboratory analyses is beneficial but not required. The successful candidate should be willing to work outdoors under field conditions as well as in laboratory, demonstrate attention to detail when collecting and recording data, and possess strong organizational and problem-solving skills. Basic knowledge of plant biology, statistics, and data management is desirable.

Research area: My research involves studies of the fundamental cognitive processes (e.g., learning, memory) that support animal behaviour, and how those cognitive processes are shaped by ecology. My research program primarily focuses on bumblebee cognition and behaviour, developing ecologically relevant assessments of cognitive abilities for use in both the lab, with commercially produced bumblebees, and the field, with wild bumblebees. My research program also includes work on the behavioural aspects of pollination and the impact of modern agricultural practices on bumblebee behaviour in open field and greenhouse agriculture.

Student roles:
For this project the student will be involved in all aspects of the project, from study design through to data analysis. This will require a literature review on the topic of bumblebee foraging under varying lighting conditions, as well as learning general information about bumblebee foraging and behaviour. The student will then participate in identifying important research questions in the field and develop a testable hypothesis regarding one of the focal behaviours identified in the project description. An experiment will then be designed to address the hypothesis identified by the student. The experiment will use the existing infrastructure in my bumblebee lab (e.g., foraging arenas and behavioural testing apparatuses), but may also involve the development of project specific apparatuses. The student will then work in a multi-member research team to run the behavioural experiment. The required activities during the experiment are variable, but are expected to include bumblebee husbandry and care, direct behavioural observation during testing, and operating simple automated data recording systems. Following data collection the student will be part of the research team involved in analyzing the data using statistical software. Throughout the project the student will also be expected to attend lab meetings with my full research group, which includes providing updates of their project and may include presentations to the lab group. At each stage of the project, the student will work directly with me, as their supervisor, as well as graduate and undergraduate students on the research team. They are not expected to have the skills required for each stage and will be trained throughout the project on task specific skills.

Skills required:
The required skills for this project are an understanding of basic biology, as taught in a university level introduction to biology course. This is expected to include knowledge of basic terms used in biology and animal behaviour research such as ecology, foraging, adaptation, and pollination. Applicants should also have an understanding of experimental design and the scientific method, as taught in a university level introduction to research methods course. This knowledge is expected to include understanding of how to develop hypotheses and predictions, awareness of the potential major pitfalls of experimental design, and an introduction to data analysis/statistics.

Research area: Our lab focuses on food chemistry and environmental research, from understanding the molecular interactions responsible for macroscopic properties to soil analysis and environmental remediation and mitigation. I work closely with the City of Orillia Waste Division to aid in the mitigation of waste-related issues.

Student roles:
This is a field- and lab-based research project focused on quantifying carbon storage and sequestration potential across representative natural assets in Orillia. The student will assess carbon-relevant ecosystem components (vegetation and soils) at selected sites and evaluate how land cover type and management conditions influence both stored carbon and potential sequestration capacity.

Key responsibilities will include: 1) Conducting field surveys at selected natural asset sites (e.g., forest, wetland, and urban green space) and establishing standardized sampling plots. 2) Measuring vegetation characteristics, including tree species, diameter at breast height (DBH), and basic canopy/ground cover estimates for use in biomass-based carbon calculations. 3) Collecting soil samples from defined depths at each site for laboratory analysis of bulk density and organic matter content. 4) Processing soil samples in the lab using standard procedures (drying, weighing, and Loss-on-Ignition or equivalent) to estimate soil organic carbon. 5) Organizing and analyzing field and lab data to calculate above- and below-ground carbon stocks across site types. 6) Comparing carbon storage and estimated sequestration potential across different land cover classes using measured data and literature-based rates where appropriate. 7) Reviewing municipal land cover and natural asset information to support scaling of site-level results to a broader spatial context.

Lastly, the student will synthesize their findings in a final report and present results in a clear technical and public-facing format. This role is well suited to students interested in ecology, environmental science, or climate change mitigation, and will build practical skills in field sampling, laboratory analysis, carbon accounting, and applied environmental assessment

Skills required:
The project is open to any undergraduate student in Science, Engineering, Economics, Urban Planning or Social Sciences field (3rd year must be completed)
The student must have excellent English (speaking and writing), be proficient in MS Excel and Word, be creative, show initiative, and be capable of producing a high-quality written report. The student must be comfortable working independently and be able to walk for extended periods during the data collection days. For safety reasons, during fieldwork, the student must have a working cell phone able to send and receive texts, make and receive phone calls, and make emergency calls.

Research area: Dr. Travis Goron’s research program sits at the intersection of plant science, microbiology, and applied evolution, focusing on harnessing and improving beneficial plant–microbe interactions for agricultural impact. His work combines field-based ecology with advanced laboratory techniques, including microbial characterization, genomics, and directed evolution. With collaborations and research experience across diverse international agricultural systems, Dr. Goron brings a globally informed perspective to sustainable crop improvement. Students in his group will engage in innovative, hands-on projects that bridge fundamental biology and real-world application, contributing to the development of next-generation microbial tools to enhance crop resilience, productivity, and environmental sustainability.

Student roles:
The student will play an active and central role in the design, implementation, and execution of both field and laboratory-based components of the project. In the field component, the student will be responsible for establishing and maintaining the on-campus trials, including planting, applying microbial treatments, and conducting regular monitoring of plant growth and phenotypic responses. They will additionally have a role in the farm co-operative. They will record observations in a systematic and reproducible manner, ensuring high-quality data collection across the growing period.

In the laboratory, the student will assist in processing plant and soil samples to identify and characterize microbial symbionts. This will include isolating microbes, maintaining cultures, and applying standard microbiological and molecular techniques under supervision. The student will also participate in downstream workflows, including preparation of samples for sequencing and assisting with directed evolution experiments.

The student will be expected to maintain organized records of experimental procedures and results, contribute to troubleshooting experimental challenges, and adhere strictly to all safety and biosafety protocols. They will take increasing ownership of components of the project as their skills develop, demonstrating initiative, attention to detail, and critical thinking.

Beyond experimental work, the student will engage with the broader scientific process by participating in regular meetings, contributing to data interpretation, and assisting with figure preparation and literature review. They will play a meaningful role in the preparation of a manuscript for publication in a peer-reviewed journal, contributing written sections and revisions in collaboration with the supervisor.

Overall, the student is expected to approach the project with curiosity, professionalism, and a willingness to learn, contributing to both the scientific outcomes and the collaborative research environment.

Skills required:
The ideal candidate will have a background in a relevant discipline such as biology, microbiology, or plant science. Prior coursework or experience in microbiology, fieldwork, or plant physiology is preferred. Familiarity with basic field and/or laboratory practices (e.g., sterile technique, pipetting) and scientific data recording is advantageous, though advanced technical skills are not required. The student should demonstrate strong organizational ability, attention to detail, and a willingness to work both independently and as part of a small team. Interest in plant–microbe interactions, experimental design, and applied agricultural research is an asset. The PI will provide training in specialized methodologies.

Research area: The Sustainable Agricultural Landscapes (SAL) Lab is dedicated to providing science that contributes to understanding the ecology of and management for an agricultural system that meets current needs without comprising the needs of future generations. While sustainable agriculture should ensure that numerous needs are met, including those that are social and economic in nature, the SAL Lab focuses specifically on those related to the environment.

Student roles:
The student will receive initial orientation to the project and the methodology they are expected to follow by the laboratory Principal Investigator. After initial orientation, a project researcher will work closely with the student to give day-to-day feedback on work completed but the student will be working in a field and lab team under the direct supervision of a field/lab team leader. The student can expect to work with the project researcher, to problem-solve as issues arise. The student will be expected to travel to the field to take soil, crop or greenhouse gas samples using our state of the art field instruments. The student could also be involved in laboratory analysis of soil and plant samples. Tasks will also include data entry and statistical analysis. The student will also have the opportunity to participate in weekly laboratory meetings, allowing the student to learn about a wide range of projects. The experience and gained technical skills will greatly aid individuals seeking to do laboratory work in the future. The project researcher will be happy to provide letters of support or recommendation for the student after successful and satisfactory completion of this position.

Skills required:
Successful applicants will possess strong organizational skills and attention to detail. A willingness and ability to problem solve and think critically is essential, as is demonstrated previous experience with field and laboratory work. Applicants' resumes should include a summary of field and laboratory course work completed and any applicable employment or volunteer experience. The applicant must be willing to work outside in adverse weather and lift 20 kg.

Research area: Nanobiomaterials, Hydrogels, Biopolymers

Student roles:
The student will contribute to the design, synthesis, and characterization of conductive nanohydrogel systems derived from wood fines. Responsibilities include preparation and fractionation of wood-fines feedstock, chemical modification of biopolymers, and fabrication of hydrogel networks using controlled crosslinking techniques.
The student will assist in integrating conductive components into the hydrogel matrix and optimizing formulation parameters to achieve targeted mechanical strength, swelling behavior, and electrical conductivity. Systematic experimental design will be used to evaluate how composition and processing conditions influence final material properties.

The role also includes comprehensive physicochemical and structural characterization of the developed hydrogels using techniques such as FTIR spectroscopy, scanning electron microscopy (SEM), rheological testing, swelling measurements, and conductivity analysis. The student will analyze experimental data to establish structure–property relationships and contribute to model development where applicable.
In addition, the student will support documentation of results, preparation of figures and reports, and contribution to scientific manuscripts and conference presentations. Collaboration with other team members working on biomass valorization and nanomaterial integration will be expected.
The position offers hands-on training in advanced biomaterials development and exposure to sustainable material innovation aligned with circular economy principles.

Skills required:
Background in chemical engineering, materials science, polymer science, chemistry, or related disciplines. Experience with hydrogels, biopolymers, or nanomaterials is an asset. Familiarity with basic laboratory techniques, solution preparation, and data analysis is required. Knowledge of characterization methods such as FTIR, SEM, rheology, or electrical conductivity measurements is considered an advantage. The ideal candidate should be detail-oriented, motivated for experimental research, and comfortable working with interdisciplinary biomaterials and sustainable material development.

Research area: Dr. Vivian Valencia is an expert in sustainable agriculture, agroecology, and food systems, with research spanning biodiversity conservation, ecosystem services, agricultural transitions, and food system governance. Her work integrates agroecology, public policy, sustainability science, and resilience thinking to understand how farms, landscapes, institutions, and communities can support environmentally sustainable, socially equitable, and economically viable food systems. Using interdisciplinary and participatory approaches, she examines interactions between ecological and social systems, with particular expertise in tropical agroforestry, biodiversity conservation, and sustainable food system transitions. Her research bridges scientific inquiry with policy and practice to inform evidence-based decision-making and transformative change.

Student roles:
The student will support a research project examining the presence of rare agrobiodiversity within the Montreal food system and the role of territorial markets in supporting its conservation and circulation. The student will contribute to both desk-based and field-based research activities designed to generate a preliminary inventory of local, heirloom, Indigenous, neglected, orphaned, and underutilized agricultural species and varieties found within the region.

A major component of the student's work will involve conducting a structured review of academic literature, policy documents, technical reports, organizational publications, and other relevant sources related to agrobiodiversity, neglected and underutilized species, territorial markets, and biodiversity conservation. The student will help identify and synthesize information on species, varieties, and food products that may be relevant to the Quebec and Montreal contexts.

The student will also participate in exploratory fieldwork activities in Montreal-area farmers' markets, specialty food retailers, public markets, and other food-system spaces. These activities will focus on observing, documenting, and cataloguing examples of rare or underutilized agrobiodiversity available to consumers. The student will help organize field observations, maintain records, compile photographs and notes where appropriate, and contribute to the development of a database of identified products and species.

Depending on the project's stage, the student may assist with stakeholder engagement, interview preparation, qualitative data organization, and the analysis of information gathered from producers, vendors, market managers, or other food-system actors. Throughout the internship, the student will work closely with the research team to identify emerging themes, summarize findings, and contribute to the preparation of research reports and knowledge mobilization materials.

The student's contributions will help establish the foundation for a larger research program investigating how territorial markets can contribute to the conservation, visibility, and revitalization of rare agrobiodiversity within sustainable food systems.

Skills required:
The ideal candidate is an undergraduate student with an interest in food systems, agriculture, biodiversity conservation, sustainability, geography, environmental studies, anthropology, or related fields. Strong observation, organizational, reading, and writing skills are important, as the project combines literature review with field-based documentation. The student should be comfortable working independently, collecting and organizing information, and engaging with diverse sources of knowledge. An interest in local food systems, farmers' markets, agrobiodiversity, and food culture is highly desirable. Experience with literature reviews, qualitative research, data management, spreadsheets, or basic fieldwork methods would be an asset but is not required.

Research area: This project is in smart agriculture, agri-food resilience, cybersecurity, and responsible AI. The research focuses on how digital tools such as sensors, farm platforms, AI decision-support systems, robotics, greenhouse technologies, livestock monitoring tools, and food traceability systems create new operational dependencies in agriculture. The project examines where cybersecurity failures, unreliable data, or AI errors may affect farm productivity, food safety, supply-chain continuity, and stakeholder trust. The work contributes to building cybersecure and reliable AgFoodTech for modern food systems.

Student roles:
The intern will work under supervision to study how digital technologies and AI systems are changing risk and resilience in modern agriculture and food systems. The student will begin by reviewing selected literature and public materials on smart farming, digital agriculture, farm automation, agri-food supply chains, cybersecurity, and AI-enabled decision support. Based on this review, the intern will identify a set of practical agri-food use cases where digital systems are becoming operationally important.

The intern will then develop structured case profiles for selected use cases, such as irrigation systems, greenhouse monitoring, dairy and livestock technologies, crop sensors, food traceability systems, and AI-based advisory tools. For each case, the student will map the key technologies, data flows, users, operational decisions, and possible failure points. The intern will analyze how cybersecurity incidents, data-quality issues, or unreliable AI outputs could affect agricultural productivity, safety, sustainability, and trust.

The student will prepare visual risk maps, tables, short case summaries, and a final report. The intern may also contribute to a practical resilience checklist for farms, food producers, or agri-food organizations. The student will meet regularly with the supervisor and research team, discuss progress, receive feedback, and present final findings in a format suitable for the SECURE-AGRO training environment.

Skills required:
The ideal student will have a background in agriculture, food science, environmental science, plant science, animal science, agri-food systems, or digital agriculture. Prior cybersecurity or programming experience is not required. The student should be comfortable reading applied research, analyzing agricultural systems, organizing information, and writing clear summaries. Interest in smart farming, food-system resilience, AI, data-driven agriculture, sustainability, or technology adoption would be highly valuable. Strong communication, analytical thinking, and curiosity about how digital technologies affect agriculture are important.

Research area: Dr. Liao’s research focuses on green infrastructure, water treatment, water resource management, plant-soil-water interactions, environmental monitoring and remediation, and remote sensing. Her work aims to optimize the monitoring and design of nature-based systems to improve water quality, plant performance, and ecosystem functions. She also investigates the design and application of sustainable, engineered materials for the removal of both traditional and emerging contaminants from water and the environment, while protecting ecosystem health. In addition, Liao employs remote sensing and data science approaches (e.g., meta-analysis) to understand, assess, and optimize vegetation, water, and environmental performance and management at multiple spatial scales.

Student roles:
The student will contribute to a larger research project examining water treatment efficiency and plant performance in green infrastructure systems under environmental stress. The role will involve supporting system setup, operation, monitoring, and maintenance, collecting and processing water and plant samples, assisting with laboratory and greenhouse or field measurements, and organizing and summarizing research data. The student will play an active role in evaluating treatment performance and plant responses while gaining hands-on experience in water quality analysis, plant assessment, environmental monitoring, and applied green infrastructure research.

Skills required:
- Academic background in environmental/chemical/civil engineering, environmental science, environmental microbiology, analytical chemistry, plant and soil science or a closely related field.
- Strong interest in applied research focused on water quality, plant science, and nature-based solutions.
- Previous research experience in laboratory analysis and greenhouse or field monitoring relevant to water or environmental systems.
- Strong analytical, organizational, communication, and teamwork skills, with attention to detail and the ability to work effectively in a collaborative research environment.

Research area: I am a professor and Canada Research Chair in Digital Agriculture at the University of Guelph, working in sustainable soil management, specifically data-driven management connecting technology, soil data, and traditional understanding of soil processes and crop production. To me, current-day multi-faceted problems need solutions with multiple dimensions. This requires an integrated and interdisciplinary approach to solving issues. With a strong background in soil science, mathematics, statistics, and sensor development, I collaborate with experts from other fields to develop multi-disciplinary projects.

Student roles:
Students will first review the literature on various image collection techniques and available computer vision algorithms used in soil science and other areas of science. Based on the available literature, students will start working on developing a new algorithm and test with theoretical images. At the same time, students will try to develop an image acquisition system using cell phone cameras to collect soil images in the field and in the laboratory. Once the system is developed, soil images will be taken in laboratory conditions. Various soil conditions will be manipulated or created to take images of soils with different soil organic matter, and soil moisture. The algorithm will then be tested on the images collected in the laboratory. Based on the challenges, the algorithm needs to be modified to improve the performance. Then soil images will be collected from field conditions and will be processed using the algorithm. Once, the algorithm is developed and tested, a cell phone app will be developed for IOS and Android operating systems.

Skills required:
A strong background in cell phone app development, computer programming, and image processing is required with desirable knowledge on image collection or photography using different types of camera, taking the photographs of soil samples, collecting and processing soil samples in the laboratory and in the field, and setting up laboratory conditions to take images. Knowledge of coding (mainly in Matlab) to automatize the image processing algorithm and image collection system using a computer is required. Critical thinking and comprehending knowledge are also required in this project.

Research area: On the Canadian prairies, the critical soil pH and/or aluminum level for maximum yield for current crop varieties is unknown; the three-way interaction (soil pH x Al levels x other factors, such as soil organic matter (SOM) levels) has not been investigated for its impact on crop yields. This project will develop a factsheet with information linking varying soil pH and/or Al levels to crop yields; producers can use this information to make decisions about which crops to rotate in fields.

Student roles:
The student will be involved in all aspects of agronomic data collection, from soil sampling to data collection and analysis under the supervision of the PI, Research Associate and Field Technical Lead.
Students will be expected to read and present in group meetings

Skills required:
-Basic knowledge in Agronomy and soil science
-Evidence on critical thinking skills
-Ability to work under field and greenhouse conditions
-Ability to multi-task
- Willingness to work flexible hours
-Willingness to learn

Research area: I am a professor and Canada Research Chair in Digital Agriculture at the University of Guelph, working in sustainable soil management, specifically data-driven management connecting technology, soil data, and traditional understanding of soil processes and crop production. To me, current-day multi-faceted problems need solutions with multiple dimensions. This requires an integrated and interdisciplinary approach to solving issues. With a strong background in soil science, mathematics, statistics, and sensor development, I collaborate with experts from other fields to develop multi-disciplinary projects.

Student roles:
Students will first review the literature on various image collection techniques and available computer vision algorithms used in soil science and other areas of science. Based on the available literature, students will start working on developing a new algorithm and test with theoretical images. At the same time, students will try to develop an image acquisition system to collect soil images in the field and in the laboratory. Once the system is developed, soil images will be taken in laboratory conditions. Various soil conditions will be manipulated or created to take images of soils with different characteristics such as soil texture, soil organic matter, and soil moisture. The algorithm will then be tested on the images collected in the laboratory. Based on the challenges, the algorithm needs to be modified to improve its performance. Then soil images will be collected from field conditions and will be processed using the algorithm.

Skills required:
A background in computer programming and image processing is required with desirable knowledge on image collection or photography using different types of cameras, taking photographs of soil samples, collecting and processing soil samples in the laboratory and in the field, and setting up laboratory conditions to take images. Knowledge of coding (Matlab/R/Python) to automatize the image processing algorithm and image collection system using a computer is required. Critical thinking and comprehending knowledge are also required in this project.

Research area: Dr. Aitazaz A. Farooque specializes in precision agriculture, climate-smart agricultural technologies, remote sensing, GIS, artificial intelligence, agricultural automation, and environmental sustainability. His research focuses on developing advanced geospatial and sensing technologies for sustainable agriculture, crop monitoring, water management, climate adaptation, and digital agriculture applications. His work integrates AI, machine learning, computer vision, GPS-GIS systems, and precision farming technologies to improve agricultural productivity and environmental management. Dr. Farooque leads several nationally and internationally recognized research initiatives at the University of Prince Edward Island and the Canadian Centre for Climate Change and Adaptation. More information: https://theatlaslab.ca/

Student roles:
The student will contribute to the design, development, and testing of an Edge-AI-based monitoring system for real-time agricultural data collection and interpretation. The student will assist in selecting suitable sensors, setting up data acquisition workflows, processing sensor outputs, and organizing field data for analysis. They will support the integration of soil, crop, and environmental measurements into a structured database that can be used for model development and visualization.

The student will also help develop machine learning models to identify crop stress conditions, detect field variability, and generate management-related indicators from sensor data. They may work on embedded or edge-computing workflows to allow real-time or near real-time data processing directly on field devices. In addition, the student will support the development of a dashboard or digital interface to display sensor readings, trends, alerts, and field-level insights.

The role will involve system testing, documentation, performance evaluation, preparation of figures and reports, and collaboration with researchers working in precision agriculture, automation, and digital farming.

Skills required:
The ideal student should have a background in engineering, computer science, agricultural engineering, mechatronics, data science, or a related discipline. Experience with Python, machine learning, sensors, Arduino, Raspberry Pi, embedded systems, IoT, data analytics, and dashboard development would be valuable. Knowledge of agriculture, soil-crop systems, sensor calibration, and field data collection would be an asset. The student should have strong analytical, programming, troubleshooting, and communication skills, with interest in smart farming, agricultural automation, and AI-based decision-support systems.

Research area: My research program aims to develop and apply advanced analytical chemistry technologies to solve challenging and emerging issues faced by the agri-food industry. Specifically, my research has been focusing on the advancement in novel and reliable chemo- and bio-sensor- and instrument-based analyses for food chemical and biological hazards as well as food adulterants; development and implement point-of-need microfluidic “lab-on-a-chip” devices to achieve real-time, cost-effective and high-throughput analysis; and development and application of mass spectroscopic based metabolomics and bioinformatics to systematically investigate food products.

Student roles:
The student will work together with a senior student in my lab for the design of RPA primers for the biological species included in the study. Optimize the DNA extraction procedures for different food products. Optimize the RPA assay and validate the specificity and sensitivity of the assay. If time permit, the student will also participate in a market survey study to authenticate the food products in the market.

In addition to perform the research work listed above, the student will also need to attend weekly group meeting and present results during the group meeting.

Skills required:
Students from biological science, food science, agriculture, botany and related areas are welcome. The student is expected to have fundamental knowledge and experience about molecular biology (particularly DNA amplification); be able to use bioinformatic tools such as NCBI to find DNA sequence, compare sequences and design primers for DNA amplification; have experience to perform DNA extraction, amplification and results readout (e.g., gel electrophoresis). Other general kills that are expected include self-learning skill, teamwork skills and communications skills (both written and verbal in English).

Research area: I am a professor and Canada Research Chair at the University of Guelph, working in sustainable soil management, specifically data-driven management connecting technology, soil data, and traditional understanding of soil processes and crop production. To me, current-day multi-faceted problems need solutions with multiple dimensions. This requires an integrated and interdisciplinary approach to solving issues. With a strong background in soil science, mathematics, statistics, and sensor development, I collaborate with experts from other fields to develop multi-disciplinary projects.

Student roles:
Students will first review the literature on various image collection techniques and available computer vision algorithms used in soil science and other areas of science. Based on the available literature, students will start working on developing a new algorithm and test with theoretical images. At the same time, students will try to develop an image acquisition system to collect soil images in the field and in the laboratory. Once the system is developed, soil images will be taken in laboratory conditions. Various soil conditions will be manipulated or created to take images of soils with different characteristics such as soil texture, soil organic matter, and soil moisture. The algorithm will then be tested on the images collected in the laboratory. Based on the challenges, the algorithm needs to be modified to improve the performance. Then soil images will be collected from field conditions and will be processed using the algorithm.

Skills required:
A background in computer programming and image processing is required with desirable knowledge on image collection or photography using different types of cameras, taking photographs of soil samples, collecting and processing soil samples in the laboratory and in the field, and setting up laboratory conditions to take images. Knowledge of coding (Matlab/R/Python) to automatize the image processing algorithm and image collection system using a computer is required. Critical thinking and comprehending knowledge are also required in this project.

Research area: My primary research area is smart and digital agriculture. My lab develops state-of-the-art digital technologies to enhance the productivity and sustainability of Canadian crop production systems. These technologies include, but are not limited to, unmanned aerial systems (or drones), ground-based robotics, multimodal sensors, Internet-of-Things (IoTs), physics-based models, and artificial intelligence (AI) approaches.

Student roles:
1. Assist with field data collection. We have regular field work led by graduate students. The prospective student is expected to assist the graduate student in field work activities, such as drone data collection, plant sampling, etc.
2. Learn and preprocess multispectral image data collected by drones. Graduate students in my team will guide the prospective students on data processing.
3. Learn and extract structural and spectral features from aerial images and build dynamic growth curves using those extracted features.
4. Summarize the results and prepare a scientific report. This report is due around the last week of the internship.

Skills required:
1. Fundamental understanding of agriculture and farming.
2. Be willing to do fieldwork.
3. Remote sensing experience is preferred.
4. Be able to conduct simple data analysis (e.g., linear regression, machine learning, etc.)
5. Strong teamwork and interpersonal skills
6. Scientific writing experience is preferred.

Research area: Dr. Wang's research group specializes in several areas of mathematical biology as diverse as modelling stoichiometry-based ecological interactions, predator-prey interactions, microbiology, spatial ecology, infectious diseases, habitat destruction and biodiversity, risk assessment of oil sands pollution. Mathematical models include nonlinear differential equations. His group has produced many highly qualified personnel. Currently the group has several postdoctoral research fellows and many graduate students.

Student roles:
The student will work on stoichiometric modelling in ecology and analyze the model numerically and mathematically to uncover the impact of abiotic factors on trophic interactions in an ecosystem.

Skills required:
Strong background in mathematical modelling and computer skills in using mathematical software packages (Matlab, Mathematica, or Maple) are required, and analytic skills (Advanced Calculus, Linear Algebra, Differential Equations) are preferred.

Research area: Most of my work is done on the mix design and characterization of recycled asphalt mixes, cold asphalt mixes, microsurfacing and pavement design. We focus mainly on bituminous materials that are used in pavement rehabilitation and on new construction that have a limited environmental footprint. Then, we work on different pavement design method to make sure that those materials are properly used.

Student roles:
The student will be in charge of the litterature review, of the material characterization and of the mix design and charcaterization. The student will also be in charge of developpig one or several characterization tests for asphalt mixes.

Skills required:
Basic knowledge on bituminuous materials

Research area: My research focuses on developing nutritional and management strategies to improve the efficiency, profitability, and environmental sustainability of beef cattle production. Using in vitro, in situ, and in vivo approaches, I investigate how rumen function, fibre utilization, feeding behaviour, and microbiome dynamics influence animal performance and methane emissions. My current work examines cattle with divergent fibre digestion capacity to identify mechanisms associated with improved feed efficiency and reduced methane production. By integrating rumen fermentation studies, microbiome analysis, and genomic tools such as GWAS, my research aims to support the development of more sustainable and productive beef production systems.

Student roles:
The student will participate in all major phases of the research project, including study planning, experimental preparation, data collection, laboratory analyses, and data interpretation. The student will assist with beef cattle feedlot growth performance studies involving individual feed intake measurements, methane emissions monitoring, and sample collection from animals, feeds, feces, and rumen contents.

Throughout the project, the student will receive hands-on training in ruminant nutrition, metabolism, and physiology, with particular emphasis on factors affecting feed efficiency, rumen function, and methane production. The student will also gain experience in ruminal fermentation and microbiology techniques, including the evaluation of rumen fermentation characteristics and microbial activity.

Laboratory training will include complete chemical analyses of feed, feces, and other biological residues, as well as the measurement and interpretation of rumen fermentation parameters such as pH, volatile fatty acids (VFA), ammonia (NH3), and methane (CH4). In addition, the student will be trained in data management, statistical analyses, and interpretation of experimental results using appropriate analytical software.

This project will provide the student with interdisciplinary research experience combining animal science, nutrition, microbiology, environmental sustainability, and quantitative data analysis within beef cattle production systems.

Skills required:
The student should be an independent, highly motivated, enthusiastic, and scientifically curious individual with an interest in the following fields: ruminant nutrition, beef production, rumen microbiology. The student is required to able to communicate effectively in English. Be coursing a Bachelor's degree in Agricultural/Animal/Veterinary Science or a closely related field. Ability to work in a group environment and being committed to collaboration, effective goal setting and evaluation, and professional accountability. Experience working with beef cattle is desired. Knowledge and experience with MS Office (Word, Excel, PowerPoint) is required.

Research area: Most of my work is done on the mix design and characterization of recycled asphalt mixes, cold asphalt mixes, microsurfacing and pavement design. We focus mainly on bituminous materials that are used in pavement rehabilitation and on new construction that have a limited environmental footprint. Then, we work on different pavement design method to make sure that those materials are properly used.

Student roles:
The student will be in charge of the litterature review, of the material characterization and of the mix design and charcaterization. The student will also be in charge of developpig one or several characterization tests for asphalt mixes.

Skills required:
Basic knowledge on bituminuous materials

Research area: Plant pathology and crop protection with a research focus on integrated pest management, crop and pathogen interactions, and biological control strategies. He has specific expertise in the identification and characterization of biocontrol agents and plant growth–promoting rhizobacteria, the genetic and molecular mechanisms underlying crop–pathogen interactions, and genome editing approaches for crop improvement and disease resistance.

Student roles:
The student will conduct experiments including seeding, V. longisporum culturing, media preparation, conidia counting, disease inoculation, transplanting, plant trait recording, seed harvesting, disease rating, and fatty acid profiling in collaboration with partners. The student is also expected to participate in regular lab meetings and actively engage with other lab members.
At the end of the project, the student will compile and analyze all collected data, and present the results in both a written report and an oral presentation during a lab meeting. The student is also encouraged to assist and collaborate with graduate students in the lab.

Skills required:
Strong knowledge of plant pathology, plant maintenance, and disease evaluation. Good writing and communication skills are required. Familiarity with Brassica napus and Verticillium longisporum is preferred. Basic knowledge of biochemistry and experience with plant physiological trait measurements are also desirable.

Research area: My research focuses on developing climate-resilient forests through tree diversity, assisted migration, and sustainable forest management. I use experimental plantations and long-term field studies to investigate how forest composition influences biodiversity, ecosystem functioning, carbon sequestration, and adaptation to climate change. The goal is to generate knowledge that supports evidence-based decisions for forest restoration, conservation, and climate adaptation.

Student roles:
The student will play an active role in the establishment and monitoring of two experimental forest plantations designed to evaluate the effects of tree diversity and assisted migration on forest resilience to climate change. Working closely with graduate students, research staff, and faculty members, the student will participate in a variety of field and research activities.

Responsibilities may include tree planting, monitoring tree survival and growth, collecting environmental and ecological data, recording field observations, and maintaining research equipment. Students may also assist with biodiversity surveys, soil sampling, and environmental monitoring activities. Depending on their interests and experience, they may contribute to data entry, quality control, preliminary analyses, and the preparation of research summaries or presentations.

The student will receive training in ecological field methods, experimental design, data management, and research safety procedures. They will gain hands-on experience working in large-scale forest experiments and learn how scientific data are collected and used to address questions related to climate change adaptation, biodiversity conservation, and sustainable forest management.

The student will be encouraged to participate in team meetings, discussions of research findings, and knowledge-sharing activities. Through these experiences, they will develop practical skills in field ecology, scientific problem-solving, teamwork, and communication while contributing to research that supports the development of climate-resilient forests.

Skills required:
Students should have a background in biology, ecology, forestry, environmental science, or a related discipline. Experience with fieldwork, plant identification, ecological data collection, or environmental monitoring is an asset but not required. Candidates should be comfortable working outdoors in variable weather conditions, including walking on uneven terrain and performing physically demanding tasks. Strong organizational skills, attention to detail, and the ability to work both independently and as part of a team are essential. Enthusiasm for forest ecology, biodiversity, and climate change research is highly desirable.

Research area: My research focuses on animal genomics and bioinformatics to understand how genomic variation influences complex traits such as health, productivity, and product quality in livestock. My research group integrates multi-omics data and advanced computational approaches to predict genomic value for improved animal health and production efficiency and identify DNA sequence variants underlying key traits in livestock.

Student roles:
Sub-project 1. Comparing Traditional Statistical Methods and Machine Learning for SNP Pre-selection
The student will compare classical statistical methods (e.g., PCA and LD pruning) with machine learning approaches for dimensionality reduction of whole-genome sequence (WGS) SNP data. The student will implement and evaluate different techniques to pre-select informative SNPs while maintaining predictive power for key traits. The student will assess performance using genomic prediction accuracy in pig and/or mink datasets. The student will also analyze computational efficiency and contribute to identifying optimal workflows for handling large-scale genomic data.

Sub-project 2. Developing Explainable Machine Learning Models for Predicting Performance and Resilience
The student will develop machine learning models (e.g., Random Forest, XGBoost) to predict performance and resilience traits in mink and pigs. The student will apply explainable AI techniques (such as SHAP values) to interpret model outputs and identify key features driving predictions. The student will compare machine learning performance with traditional linear models using existing phenotype and genotype data. The student will also generate visualizations to communicate results and support biologically meaningful interpretation for livestock breeding applications.

Skills required:
The students should have a background in either machine learning/deep learning or animal genetics/genomics. The students require a basic understanding of programming and the Linux environment.

Research area: Beef production, pasture management, agroforestry, forages, GHG emissions, biodiversity

Student roles:
During a trial aimed at quantifying methane
emissions from grazing cattle, the candidate
will be responsible for monitoring grass
growth and quality throughout the
experiment and assist the teams manage the
Greenfeed emission monitoring system .
Tasks will include sampling grass before and
after each paddock change and estimating
botanical composition (proportion of
legumes, grasses, and forbs). To document
grazing conditions during the experiment, the
candidate will also keep a journal integrating
field observations, data from weather
station, and information provided by the
farmer. Sample stabilization through drying
will also be carried out by the candidate.
These activities will be crucial for estimating
the dry matter intake of the animals (DMI)
and thus correlating the measured methane
quantities (g CH4/day) with feed intake (g
CH4/kg DMI). Additionally, the student will
assist the rest of the team in preparing forage
samples for quality analysis using nearinfrared spectroscopy (NIRS).

Skills required:
The candidate must have good
communication and planning skills to
coordinate tasks with other project
stakeholders (farmers, technicians, and
graduate students). An ability to work
outdoors and adapt to changing conditions is
essential, particularly with a variable
schedule depending on weather and specific
farmer constraints. Additionally, the
candidate should be able to solve problems
independently and work effectively in a team
to achieve project goals. Previous experience
in an agricultural or research environment
would be an asset.

Research area: Our specialized research area focuses on identifying and utilizing the natural diversity within the saskatoon berry germplasm of University of Saskatchewan. Specifically, it involves investigating how genetic variation manifests as phenotypic variation in saskatoon berry. By understanding these relationships, we can accelerate the development of cultivars with superior yield and fruit quality.

Student roles:
The student will be responsible for assisting in crossing in the field and ploidy analysis of saskatoon berry using the optimized flow cytometry protocol. They will also be assisting with phenotypic data collection in the field, as well as harvesting berries and fruit quality trait measurement in the lab. Overall, they will contribute to the variability assessment of saskatoon berry for both genetic and phenotypic means.

Skills required:
The student should have a background in plant sciences, agriculture, or a related field. While prior experience with basic lab tools or fieldwork is an asset, this position prioritizes a candidate’s willingness to learn over an established checklist of skills. Motivated students who are genuinely excited to learn new technical methods are highly encouraged. The candidate should also demonstrate good diligence and the ability to work independently as well as in a research team.

Research area: This specialized research area focuses on the generation of phenotypic data to identify potential haskap lines maintained within the USask Fruit germplasm that exhibit increased fruit firmness, along with other desirable traits. We are implementing high-throughput phenotyping techniques and technologies for the scalable evaluation of berry firmness. The research involves fieldwork, computational analysis, and genetic analysis.

Student roles:
The candidate will be responsible for assisting in the generation of high-throughput phenotyping data related to haskap berry firmness throughout the late spring and summer. The role will involve following standardized procedures and data collection in both laboratory and field settings. The student will assist with collecting berries from the field at various harvest dates and managing the storage for future evaluations. Work assignments will be prioritized based on project needs; the candidate should be prepared to assist with the most pressing tasks within the Fruit Program. The student will also participate in routine laboratory meetings and activities.

Skills required:
The student should have a background in plant sciences, agriculture, biotechnology, or a related field, with an interest in evaluating plant phenotypic data. Previous experience in field, greenhouse, laboratory, and computational settings would be considered an asset. The candidate should demonstrate strong attention to detail, organizational skills, and effective communication within a team setting. The work will require the ability to work independently while also contributing collaboratively as part of a research team.

Research area: I am a monogastric nutritionist specializing in poultry and swine nutrition. I conduct research on feed ingredient evaluation and nutritional strategies to promote gut health and resilience in the absence of antibiotics.

Student roles:
Drying of fruit pomaces
Feeding and weighing of chickens
Data collection and record keeping

Skills required:
Ability/willingness to work in a lab environment
Ability/willingness to work in a team environment
Ability/willingness to work with poultry.

Research area: This research lab specializes in perennial fruit crops and their wild relatives. We work at the intersection of plant agriculture and data analytics to quantify and characterize trait and genomic variation. Ongoing work includes projects on apples, strawberries and blueberries, some of the most important fruit crops in both Canada and globally. We apply computational tools to diverse dataset improve our understanding of fundamental plant biology and provide evidence-based recommendations for plant breeding, management, and conservation strategies.

Student roles:
The student will contribute to the study of the evolution of cold resistance and quality-related genes in species from the Rosaceae family. First the student will complement an existing database of candidate genes using literature review and bioinformatics tools for sequence analysis. Once defined a set of candidate genes, the student will gather publicly available genomes form species growing in diverse climatic regions and evaluate the quality of the available data. Subsequently, the student will apply multiple sequence alignments and phylogenetics analyses to reconstruct the evolutionary history of the selected genomes. With the analyses and the candidate genes database, the student will conduct comparative analysis and statistics aimed at identifying nucleotide diversity, conservation, and signatures of selection. The activities rely on the use of the HPC interphase and R programming for analysis and visualization. The student will document all workflows using reproducible research practices and write a final report summarizing their results and conclusions. All data and code will be organized for future lab use.

Skills required:
The student is expected to have a background in biology, preferably in plant biology, botany, bioinformatics or a related field. The student should have basic to intermediate knowledge with computer programming to be comfortable developing the required analyses. To be able to place the results of their work in a broader context and facilitate impactful training, familiarity with literature review, analysis and writing is expected. The student will be encouraged to have scientific discussions for research planning and work independently as well as part of a team. The student should be organized, detail-oriented, and have strong time management skills.

Research area: I am a soil scientist with research interests in soil health, soil fertility, land reclamation and remediation.

Student roles:
Students (2 Mitacs student) will work together to set up the experiment and monitor plant growth.
Students will also analyze soil properties such as pH and EC. Measure plant length during the experiment, and biomass at the end of the study.
Students will provide a report and presentation at the end of the internship

Skills required:
Students should have organizational skills, attention to details, be focused, good written and communication skills, and be able to set up and monitor experiments.

Research area: Professor Jitendra Paliwal is the Vice-President, Research and Innovation at the University of Winnipeg. His internationally recognized research program at the University of Manitoba, where he holds an Adjunct Professor appointment, specializes in the post-harvest preservation, handling, and quality assessment of cereal grains, oilseeds, and leguminous crops. With over 285 peer-reviewed publications, $14 million in research funding, and an h-index of 49, Dr. Paliwal’s research utilizes electromagnetic imaging, digital twins, vibrational spectroscopy (Raman and FTIR), and machine vision. His lab aims to optimize quality monitoring and processing through spectral fingerprinting and microstructural analysis.

Student roles:
You will begin with a structured literature review covering FTIR spectroscopy of pulse flours, chemometric and machine learning approaches for food quality assessment, and the agronomic and commercial context of Canadian lentil market classes. This foundation will inform experimental design, preprocessing strategy selection, and model architecture decisions. You will actively participate in sample preparation, including the milling of lentil seed lots into flour using laboratory-scale dry milling equipment, and in spectral data collection using the lab’s Bruker ATR-FTIR spectrometer. You will follow rigorous protocols for sample handling and meticulous documentation of sample metadata, including market class, cultivar identity, and measurement conditions, to ensure a reproducible and well-characterized dataset. You will take a leading role in data analysis, implementing spectral preprocessing pipelines including Standard Normal Variate correction and mean centering, performing Principal Component Analysis for exploratory visualization, and developing the three-tier modelling framework: classical PLSR and PLS-DA models as the interpretable baseline, an optimized pipeline combining CARS variable selection with Ridge regression and Support Vector Machines, and a Random Forest model with SHAP explainability for wavelength-level interpretation. Model performance will be evaluated using standard chemometric metrics, including R², RMSE, and classification accuracy on held-out external validation sets. You will also contribute to protein secondary structure analysis through second-derivative spectra and Gaussian curve-fitting of the amide-I region, and to starch crystallinity characterization through FTIR absorbance ratios at 1046/1016 cm-1 and 1016/993 cm-1. You will maintain detailed laboratory records, ensure compliance with safety protocols, and troubleshoot any instrumentation or modelling challenges. Regular communication with the principal investigator and fellow team members will be expected to coordinate experimental activities and interpret results. You will actively contribute to manuscript preparation, assisting with drafting scientific publications that report on methodology, model performance, and practical implications for lentil quality screening in the Canadian pulse processing sector.

Skills required:
The ideal student should possess a strong background in food science, agriculture, biosystems engineering, chemistry, or a related field. Familiarity with spectroscopic techniques, particularly infrared spectroscopy, and experience with pulse crop quality assessment would be an asset. Knowledge of chemometric or machine learning methods, including regression and classification modelling, as well as spectral preprocessing, is desirable. Proficiency in Python for data analysis and statistical computing is expected. Excellent analytical and problem-solving skills, including the ability to interpret complex spectral datasets, are essential. Effective verbal and written communication skills, along with the ability to work collaboratively within an interdisciplinary research team.

Research area: I am a professor and Canada Research Chair at the University of Guelph, working in sustainable soil management, specifically data-driven management connecting technology, soil data, and traditional understanding of soil processes and crop production. To me, current-day multi-faceted problems need solutions with multiple dimensions. This requires an integrated and interdisciplinary approach to solving issues. With a strong background in soil science, mathematics, statistics, and sensor development, I collaborate with experts from other fields to develop multi-disciplinary projects.

Student roles:
Students will first review the literature on various aspects of soil spectroscopy. Responsibilities will include processing and preparation of soil samples, collection of spectral measurements in the laboratory, setting up experiments to collect spectra at different moisture content, data entry and organization, data processing and management, data analysis, and soil property prediction.

Skills required:
A strong background in handling soil samples in the laboratory and in the field. Background on measuring soil properties in the field and collecting soil samples for measurement in the laboratory is required. Experience in using some instruments in the laboratory including spectrometers will be an asset. Critical thinking and comprehension of knowledge are also required in this project. Developing predictive models using regression-based and machine learning-based techniques will also be an asset.

Research area: Dr. Hailong He's research interests are focused on: 1. Soil physics/critical zone hydrology in cold and semi-arid/arid regions 2. Experimental and numerical study of fate and transport of mass and energy 3. Land surface/climate modelling of water, heat and carbon fluxes 4. Data assimilation and digital mapping of soil hydrothermal properties 5. Remote sensing based soil erosion risk mapping and hydrology 6. Soil physics and hydrology related sensor and software development

Student roles:
Spanning micro-scale mechanisms to regional-scale processes, from advanced next-generation sensor development, X-ray micro-computed tomography and geophysical techniques to proximal/remote sensing, from site monitoring to field application and regional land surface modelling, from geo-statistics to artificial intelligence and big data analysis, our program offers an interdisciplinary and integrated training platform that prepares MITACS GRI interns across the full research-to-application continuum for experiential learning and career readiness.

The Conduct a focused review of scientific literature, assist with processing and quality control of soil and climate datasets, apply statistical, computational, or AI methods to analyze data, prepare figures, summaries, or short reports documenting methods and key findings. Contribute to a final internship report and, where possible, to conference abstracts or draft manuscripts.

Skills required:
Undergraduate students with interests/backgrounds in the following disciplines are welcome to apply:

Interests: Soil physics; Freeze–thaw processes / numerical modeling of water–heat–solute transport; Land surface processes / climate modeling; Geographic information systems (GIS) / land resource management / surveying and mapping; Data assimilation; AI / deep learning / machine learning; Electronic engineering / instrument development
Disciplines: GIS / Remote Sensing / Land Resource Management / Physical or Human Geography; Surveying and Mapping; Environmental Science / Engineering; Soil Science; Soil and Water Conservation; Agricultural Water and Soil Engineering; Hydrology, Electrical Engineering; Mathematics / Physics; Computer Science / Software Engineering

Research area: Narendra Malalgoda is an Assistant Professor of Supply Chain Management at the Asper School of Business, University of Manitoba. He completed his Ph.D. in Transportation and Logistics, with emphasis on Logistics and Supply Chain Systems, in 2020 and holds an MSc in International Agribusiness, both from the North Dakota State University, USA. Before joining the Asper School of Business, Dr. Malalgoda completed his post-doctoral training in the Department of Agribusiness and Agricultural Economics at the UofM. Dr. Malalgoda is the Associates fellow in Supply Chain Management 2024-2027.

Student roles:
The student's primary role is to lead the Indigenous-focused stream of the food-access analysis, ensuring the broader project meaningfully reflects the realities of First Nations, Inuit, and Métis communities. Working from the national grocery store database and GIS analysis developed in the parallel streams, the student will focus specifically on food access in and near Indigenous communities.
The student will compile spatial data on the locations of First Nations reserves, Inuit Nunangat regions, and Métis settlements and overlay it with grocery and food retail locations. They will measure and map access, identifying communities with no nearby full-service grocery store, long travel distances, or seasonal road limitations and characterizing where gaps are most severe.
Importantly, the student will situate this analysis within an appropriate ethical and cultural framework. They will familiarize themselves with Indigenous data sovereignty principles, including OCAP®, and ensure the work is framed as a partnership rather than extraction. Where the project involves engaging communities or community-held data, the student will help follow respectful protocols under the supervisor's guidance. They will clearly document the limitations of a retail-based measure, noting it does not capture the traditional and country food systems central to Indigenous food security.
The student will produce maps, summary statistics, and a written analysis highlighting Indigenous communities facing the greatest food-access challenges, along with context that supports culturally appropriate interpretation. They will collaborate with the supervisor and the other interns, flagging where Indigenous-specific considerations should shape the overall methodology.
Through regular check-ins, the student will refine their approach and report progress. By the end of the term, they will deliver a focused set of maps, an analytical summary, and recommendations for ethical next steps. This role offers meaningful, applied experience in equity-centred spatial research.

Skills required:
The student should have a basic familiarity with GIS tools and be comfortable with spreadsheets and data handling. An interest in and respectful awareness of Indigenous histories, rights, and contemporary issues in Canada is important; lived experience or community connection is an asset and is warmly welcomed. Understanding of food-security concepts is helpful. Basic geographic knowledge is expected, and exposure to census or community data is beneficial. Skills in Python or R are optional. Most important are cultural sensitivity, humility, strong communication, and a commitment to ethical, community-centred research. A background in geography, Indigenous studies, public health, or planning is

Research area: The nexus of community development (CD) and ecosystem restoration provides opportunities to build prosperity around food and other natural resources. We will work on food forests, university campuses, ecosystem restoration and student food security. The focus is on native food plants to build ecosystem restoration, culturally appropriate capacity building, youth employment opportunities and researching best practices to grow and harvest health on campus. Food-based development and training is a purposeful intervention that can educate students about the global biodiversity framework of restoring 30% of land by 2030 (30x30). 30x30 for university campuses can transform education sites from monoculture to biodiversity.

Student roles:
Students will engage in garden and food forest projects at the University, focusing on native plants and biodiversity. These ecosystem restoration projects will examine and compare the productivity, temperature and biodiversity of food forests compared to the typical green grass on campus. We will examine biodiversity for bats, birds, frogs and other animals noted from acoustic monitors, trail cameras and iNaturalists. The location on campus makes this workplace a safe and convenient place to measure biodiversity and food production.

Skills required:
The student would have the skills/background or interest in:
- Interest and skills in gardening, agriculture, permaculture, ecosystem restoration, arbiculture or seed saving (some training will also be provided)
- Interest in food security, community economic development and capacity building of rural communities
- Good communication skills
- Computer and internet skills.
- Co-ordination and time management skills.
- Video and recording skills to capture stories and create how-to videos.
- Reliable.
- Able to complete tasks.
- Able to work well with people.

Research area: Our team works mostly on plant aroma and how to create new cultivars with a unique flavor. We also work on the factors controlling flowering, pigmentation, and disease resistance—essential aspects in breeding horticultural crops. Our current projects focus on strawberries, potatoes, and tomatoes. We use genetic and biochemistry tools to better understand plants biology. Our work also includes field studies where we evaluate new lines from our breeding programs. Key words: Horticultural science, plant volatiles, aroma, flavor, flowering, disease resistance, breeding, agriculture, biology, biochemistry, plant genetics.

Student roles:
The student will be part of a scientific team including research associates and graduate students. He will have the occasion of learning from the expertise of several lab members. He will be working closely with a graduate student, offering a good opportunity to learn from him. The student will have his own little project with objectives, but he will also learn different techniques that we often use in the lab. Among these, are:
- Molecular biology (DNA extraction, PCR, CRISPR, etc.)
- Genotyping (HRM)
- Plant volatiles analysis (gaz chromatography)
- Managing field experimental plots
- Plant tissue culture
- Genomic data analysis (GBS, RNAseq, bioinformatic)

The student will be provided scientific papers to learn on his subject and will be encouraged to explore by himself the latest literature to discuss with the other team members about the future of the field. He will analyze the data of his project and will discuss with the team about where the project could or should go after. The lab has several grants that can provide future opportunities for graduate studies.

Skills required:
The student must have sufficient background knowledge in plant biology and genetics. Programs in agriculture, biology or biochemistry would probably provide a good background for the project but other disciplines could be appropriate. Basic lab techniques experience would be an asset (e.g. molecular biology).

Research area: Narendra Malalgoda is an Assistant Professor of Supply Chain Management at the Asper School of Business, University of Manitoba. He completed his Ph.D. in Transportation and Logistics, with emphasis on Logistics and Supply Chain Systems, in 2020 and holds an MSc in International Agribusiness, both from the North Dakota State University, USA. Before joining the Asper School of Business, Dr. Malalgoda completed his post-doctoral training in the Department of Agribusiness and Agricultural Economics at the UofM. Dr. Malalgoda is the Associates fellow in Supply Chain Management 2024-2027.

Student roles:
The student's primary role is to carry out the GIS-based spatial analysis that turns the grocery store database into insight about food access across Canada. Working from the cleaned, geocoded dataset produced in the parallel data-development stream, the student will design and run analyses to measure grocery accessibility and identify food deserts.
Early in the term, the student will assemble the supporting datasets needed for analysis, including population and socioeconomic data from the Census, administrative boundaries, road networks, and transit information where available. They will ensure these layers are correctly projected and aligned with the grocery store data.

The core analytical work involves measuring access. The student will apply GIS techniques such as buffer analysis around store locations, network-based travel distance and travel time calculations, and spatial joins between store access data and population data. They will define and apply criteria for what constitutes a food desert, combining distance to stores with demographic factors such as income, and adapt these criteria appropriately for urban, suburban, rural, and remote contexts.

The student will then identify and characterize problem areas: neighbourhoods or regions with poor access and populations that are disproportionately affected. They will produce clear, well-designed maps, charts, and summary statistics communicating these findings at local, provincial, and national scales.

Throughout, the student will collaborate with the supervisor and the intern responsible for the database, giving feedback on data structure and flagging quality issues that affect analysis. Regular check-ins will track progress and refine the analytical approach. Toward the end of the internship, the student will deliver a set of maps, an analytical summary of findings, and documentation of the methods used.

This role provides strong, applied experience in spatial analysis, data integration, and communicating geographic research.

Skills required:
The student should have a basic working knowledge of a GIS platform such as QGIS or ArcGIS, including making maps and performing simple spatial operations. Comfort with spreadsheets and basic data handling is expected. Familiarity with geographic concepts, coordinates, projections, buffers, and spatial joins is helpful. Some exposure to demographic or census data is an asset. Basic Python or R skills (for example, GeoPandas) are beneficial but not required. The student should be detail-oriented, curious, and able to communicate results clearly through maps and visuals. A background in geography, planning, environmental studies, data science, or public health is welcome.

Research area: The global demand for vegetable oils is rising for use in food, biomaterials, and biofuels. Enhancing seed oil production and customizing oils can boost Canada’s economy and diversify markets. Certain microorganisms also produce valuable lipids. However, improving oil yield and quality remains challenging due to limited knowledge of biosynthesis. Our research focuses on (1) expanding understanding of storage lipid formation, (2) increasing oil yield in plants and microbes, producing high-value bioproducts via synthetic biology, and (3) improving agronomic traits of canola like stress tolerance, seed yield, and oil quality to ensure stable, sustainable oil crop production amid environmental variability.

Student roles:
The students will conduct research with postdoctoral fellows and PhD students and get strong hands-on training. The students will do research with senior researchers and need to follow advice.

Skills required:
Demonstrated strong knowledge and course scores in plant physiology, genetics and plant science. Excellent organizational abilities and communication skills (both written and verbal) are essential, as is a proven capacity for effective collaboration. Previous research experience in labs is advantageous.

Research area: My research focuses on plant–soil microbiome, ecology, soil biodiversity, and ecosystem resilience across agricultural, forest, and urban ecosystems. The Plant–Soil Microbiome Ecology and Innovation Lab at UBC integrates ecology, soil science, plant science, microbial ecology, molecular biology, environmental DNA, bioinformatics, computational approaches, and spatial analysis to understand how soil and root-associated microbiomes contribute to plant health, nutrient cycling, ecosystem recovery, and climate adaptation.

Student roles:
The intern will conduct structured literature searches, screen relevant papers, extract information into a database, summarize microbial response patterns, participate in regular discussions with the supervisor and lab team, and prepare a final synthesis report and presentation. Depending on progress, the intern may also contribute to figures, conceptual diagrams, or a manuscript outline. There will be plenty of opportunities for filed visits for interested candidate.

Skills required:
The student should have a background or strong interest in ecology, microbiology, environmental science, soil science, plant science, climate change biology, or a related field. Strong reading, writing, organization, and attention to detail are important. Experience with spreadsheets, reference management software, R, or evidence synthesis methods is an asset but not required.

Research area: My research integrates hydroclimate science, Earth observation, machine learning, and data science to support climate resilience, food security, and water-resource decision-making. I develop open research datasets, forecasting models, and decision-support tools that translate climate and satellite information into actionable insights for agriculture, disaster risk, and environmental management.

Student roles:
The student will work as a research intern developing a reproducible data science workflow for crop forecasting and food-security applications using HarvestStat and related climate and satellite datasets. At the beginning of the internship, the student will review key literature, become familiar with the HarvestStat platform, and identify the target crop, region, and forecasting problem in consultation with the supervisor.
The student will prepare analysis-ready datasets by combining subnational crop statistics with selected climate and Earth observation variables. This work may involve data cleaning, harmonization, quality control, missing-data checks, feature engineering, and visualization. The student will write scripts to process and analyze the data, document each step, and organize the workflow so that it can be reused or extended by other researchers.
In the modeling stage, the student will test preliminary crop yield forecasting approaches, compare model performance using metrics such as RMSE, R-squared, or MAPE, and examine which climate or satellite variables are most useful for forecasting. The student will prepare figures and tables summarizing the data, model results, and implications for food-security assessment.
The student will work closely with graduate students in the Hydroclimate and Society Analysis Lab and participate in regular lab discussions. The student will also have weekly meetings with the faculty supervisor to discuss progress, troubleshoot technical issues, refine the research plan, and interpret results. These interactions will provide mentoring, feedback, and opportunities to learn how collaborative research is conducted within a broader international research consortium.
By the end of the internship, the student is expected to deliver a cleaned dataset or data-processing workflow, documented code, model results, visualizations, and a short final report or presentation. If the results are sufficiently developed, the student may be invited to contribute to a manuscript or open-source research product associated with HarvestStat.

Skills required:
The student may have a background in data science, computer science, engineering, geography, agricultural science, climate science, environmental science, statistics, or a related field. Experience with Python is strongly preferred. Familiarity with machine learning, geospatial data, climate datasets, crop statistics, or reproducible research workflows would be an asset.

Research area: I am a Research Scientist at the Prairie Swine Centre, leading the ethology and welfare group. I am also an adjunct professor at the University of Saskatchewan and part of the Swine Welfare research team at the Western College of Veterinary Medicine. My background is multidisciplinary at the interface of applied animal behaviour, animal welfare and social science with the following specialised research areas: • Improvement of rearing environment for pigs • Social communication and structure of pigs • Environmental enrichment for farm animals

Student roles:
The students are responsible for assisting the experimental housing, data collection through behaviour observation and other sow health, welfare and productivity measures, and data management. Depending on the students’ performance and interest, some preliminary statistical analysis and writing up the results in a scientific format can be done under the supervisor’s guidance. Students are encouraged to complete a manuscript for scientific publication if it will help the students’ future career development. There may also be other opportunities to assist with other pig husbandry practices taking place at the research farm and to help with other ethology research projects. The supervisor is also open to mentor the students to develop their future goals to advance their career.
Timeline of the project:
Week 1: Induction, training, literature review and protocol preparation
Week 2: Experimental set-up, data collection training
Week 3 – 11: Working with a PhD student on the experiment and data collection
Week 12: Data analysis, results write-up and completion of tasks to bring the project to a close

Skills required:
The students should have basic understanding of farm animal behaviour and welfare, and ideally have some knowledge on pig production. However, students who are interested in farm animal welfare but are from social science or humanity backgrounds are also welcome to apply if they have some prior experience working on animal behaviour and animal welfare subjects. We are looking for highly motivated students who care about animals and are not afraid of working with live, large farm animals on production farms.

Research area: I am a professor and Canada Research Chair in Digital Agriculture at the University of Guelph, working in sustainable soil management, specifically data-driven management connecting technology, soil data, and traditional understanding of soil processes and crop production. To me, current-day multi-faceted problems need solutions with multiple dimensions. This requires an integrated and interdisciplinary approach to solving issues. With a strong background in soil science, mathematics, statistics, and sensor development, I collaborate with experts from other fields to develop multi-disciplinary projects.

Student roles:
Students will first review the literature on various image collection techniques and available computer vision algorithms used in soil science and other areas of science. Based on the available literature, students will start working on developing a new algorithm and test with theoretical images. At the same time, students will try to develop an image acquisition system using cell phone cameras to collect soil images in the field and in the laboratory. Once the system is developed, soil images will be taken in laboratory conditions. Various soil conditions will be manipulated or created to take images of soils with different soil organic matter, and soil moisture. The algorithm will then be tested on the images collected in the laboratory. Based on the challenges, the algorithm needs to be modified to improve its performance. Then soil images will be collected from field conditions and will be processed using the algorithm. Once, the algorithm is developed and tested, a cell phone app will be developed for IOS and Android operating systems as well as web-based platforms.

Skills required:
A strong background in cell phone app development, computer programming, and image processing is required with desirable knowledge on image collection or photography using different types of camera, taking the photographs of soil samples, collecting and processing soil samples in the laboratory and in the field, and setting up laboratory conditions to take images. Knowledge of coding (mainly in Matlab) to automatize the image processing algorithm and image collection system using a computer is required. Critical thinking and comprehending knowledge are also required in this project.

Research area: Dr. Vivian Valencia is an expert in sustainable agriculture and food systems, with a background in agroecology, public policy and sustainability transitions, she explores transition pathways towards sustainable and fair food systems. She combines systems thinking, participatory research, and interdisciplinary approaches to understand how farms, communities, policies, and institutions can contribute to resilient food systems. Her work bridges academic research and practical action,

Student roles:
The student will support a research project exploring food and farming as public goods and public systems. Their primary responsibilities will include conducting literature reviews, identifying and analyzing relevant case studies, and synthesizing findings from academic articles, policy reports, government documents, media sources, and organizational websites. The student will help develop a database of examples from across the food system, including public grocery stores, food hubs, processing facilities, procurement programs, and income-support initiatives. Depending on the project's stage, the student may also assist with interview preparation, qualitative data analysis, and report writing. The student will contribute to producing research outputs that help conceptualize alternative models for organizing food systems.

Skills required:
The ideal candidate is interested in food systems, agriculture, social justice, public policy, sustainability, or related fields. Strong reading, writing, and analytical skills are essential, as the project will involve reviewing and synthesizing academic literature, policy reports, and other sources. The student should be curious, self-motivated, and comfortable working independently while receiving regular guidance. Experience with literature reviews, qualitative research, interview analysis, Zotero, Excel, or basic policy analysis would be an asset but is not required. An interest in exploring innovative approaches to food-system transformation is highly desirable.

Research area: I am a professor and Canada Research Chair at the University of Guelph, working in sustainable soil management, specifically data-driven management connecting technology, soil data, and traditional understanding of soil processes and crop production. To me, current-day multi-faceted problems need solutions with multiple dimensions. This requires an integrated and interdisciplinary approach to solving issues. With a strong background in soil science, mathematics, statistics, and sensor development, I collaborate with experts from other fields to develop multi-disciplinary projects.

Student roles:
Students will first review the literature on various aspects of soil spectroscopy. Responsibilities will include processing and preparation of soil samples, collection of spectral measurements in the laboratory, setting up experiments to collect spectra at different moisture content, data entry and organization, data processing and management, data analysis, and soil property prediction.

Skills required:
A strong background in handling soil samples in the laboratory and in the field. Background on measuring soil properties in the field and collecting soil samples for measurement in the laboratory is required. Experience in using some instruments in the laboratory including spectrometers will be an asset. Critical thinking and comprehension of knowledge are also required in this project. Developing predictive models using regression-based and machine learning-based techniques will also be an asset.

Research area: Crop roots are the bioengine that feeds the world. Yet farmers have few tools to assess root health and how their roots vary across their fields, hindering their ability to effectively deliver nutrients and other resources in an efficient manner. The Proctor lab combines geomatics, engineering, computer science, and biology to the study of root systems in agricultural systems to widen our observational windows to discover how roots operate, how management practices affect roots, and how roots can be optimized to improve agricultural productivity worldwide.

Student roles:
Student(s) will work towards building practical supports for the creation of root surveillance networks. The apex of supports is a software tool capable of recommending sensor placement based on known and unknown field scale factors. Student(s) will co-develop this software with input from on-the-ground users to ensure that the interface is intuitive for a diverse user range while still offering functionality to users seeking advanced controls. User centric design is key. Student(s) will be tasked with connecting with users to solicit their input as well as presenting their finished products for feedback. Alternative or supplemental support resources may be developed such as user manuals, videos, whitepapers as needed to ensure the developed supports meet the needs of farmers. Project success is set as the removal of all technological barriers to adoption.

Student(s) will take a hand-on approach to the testing and validation of the networked RootVision system. A high-density sampling network will be deployed to capture the spatial-temporal dynamics of Rhizoctonia solani spread in the field. Deployed sensors maybe moved as needed to effectuate comprehensive data colllection. Based on the quantified spread rates, alternative lower density sampling setups will be evaluated using simulation models to evaluate their efficacy and uncertainty. Using spatial analysis, the best deployment setups that balance affordability and accuracy will be determined. Findings will be incorporated into recommendations to improve the uptake of the RootVision system in the farming community.

Skills required:
Student(s) should have a background in agriculture, geomatics, and/or computer science. Good communication skills are required in order to liaise between different stakeholders, to listen to their concerns, and take a leadership role to bring their voices to the forefront to make meaningful impacts that lead to project success. Critical thinking skills are needed to integrate quantifiable and non-quantifiable data into practical solutions that work for diverse user groups. Being able to situate oneself in the circumstances of other groups to make recommendations that balance practicality, accuracy, and affordability are needed. Independence to conduct field work is an asset.

Research area: My research program in the Niagara integrates aspects of ecology and environmental sciences to examine ecosystem responses to introduction of native plant species. Working with organic vineyards, we are testing how biodiversification can help the ecosystem to better adapt to climate change, especially extreme events such as drought or flooding. The overall theme is how to enhance sustainable agriculture using an ecosystem approach that links multi-trophic levels to reduce environmental and climatic stress due to pest invasions and climate extremes while promoting biodiversity conservation. Plant and invertebrate responses to environmental stresses are surveyed on a regular basis during the summer.

Student roles:
The intern/research assistant will have the opportunity to be involved is the project on sustainable alternatives in operating vineyards of the Niagara region through cover crops, and perimeter planting. This project includes graduate students who examine the various components of the vineyard ecosystems (two vineyards) from weather conditions, soil sampling, plant survey and monitoring of cover crops, invertebrate surveys, as well as rootstock and grape/vine. In addition, a landscape approach will be used where the importance of the perimeter plantings will be measured through surveys. The person will be involved with these two types of experimental design. He will learn a lot about management skills since in the field, we often must vary our schedule according to the weather conditions. As the field work is done as a team, effective communication, organization and time management skills will be discussed at the start of the internship. And the intern will learn about field and lab safety. The intern will be supervised by me and my lab manager/assistant, and interact with my graduate students. Other aspects that the intern will be involved and learned on are experimental design and standardized sampling protocols including for soil, plants and invertebrates; scientific method and linking objectives of the projects to the hypotheses being tested; management of insect collection, and plant species surveys; basic plant and insect taxonomy (basic to medium skills, depending on previous acquired skills); data entry and research data management. One of the important aspects of any research project is to be able to learn how to appropriately collect data and then enter them on the computer in a way that data can be analyzed later or retrieved from year to year in a long-term project including the issue of data quality and assurance and how to ensure that this is done correctly.

Skills required:
This project can be undertaken by a senior undergraduate or recent graduate in the field of Biology or Agricultural Sciences. Field work experience is a good asset since the intern will be involved in surveys in the vineyards. A strong knowledge in ecology and/or agriculture is important and ideally with some experience in field methods or species identification. Knowledge in research methodologies would be an asset. As this project is highly collaborative, team work ethics and good communication skills are important. Since the field work depends on the weather, flexibility in the schedule is also an advantage.

Research area: Soil science/environmental science/Global change research

Student roles:
The intern will be involved in data collection, assisting in conducting fieldwork (e.g., sample collection and field measurement of crop growth etc), laboratory sample analysis and data entry/analysis. The intern will have opportunities to gain practical experience and understand the broader implications of the research. The majority of the research will be done in the lab, but up to 40% of the intern’s time could be spent doing fieldwork.

Skills required:
Education in soil science, chemistry, environmental science, biology, and/or agriculture. Experience working in an inorganic chemistry laboratory and in the field (agricultural/ environmental). Ability to perform repetitive processes. Ability to work in the field. First-air training. Training on laboratory and field techniques will be provided.

Research area: Learn beekeeping and improve pollinator health with science! We study the diagnosis and treatment of honey bee disease and the role of honey bees in sustainable agriculture, using a mix of field and laboratory experiments. Together with our team of 20+ students, we manage 250 honey bee colonies for research. You will learn and develop skills and expertise in beekeeping, honey bee biology, microbiology, molecular biology, and data presentation and analysis. Our lab has successfully hosted ~10 Mitacs interns in the past, 2 of which have returned to pursue MSc studies in our lab. We welcome you to join us!

Student roles:
The student will work closely with a graduate student mentor and the supervisor to design and execute the experimental treatments, collect the data, and analyze and present the results. Specifically, the student, with assistance from the graduate student, will prepare the antibiotic treatments, manage and care for the experimental honey bee colonies, prepare and infect the colonies with Melissococcus plutonius bacterial culture, and monitor larval survival in each colony.

Skills required:
Students must not have bee sting allergy and be physically fit. A valid driver’s license is an asset. Students with a background in biology, microbiology, toxicology, agriculture, apiculture, veterinary medicine, animal science or entomology are ideal for our lab. Students should enjoy working in both the laboratory and in the field with a large, diverse team of graduate students, postdocs and undergraduates. Students do not need prior beekeeping experience. Students are required to demonstrate curiosity, enthusiasm, and a strong work ethic, combined with collegiality and respect for others, and concern for the safety of themselves and others.

Research area: My research program aims to conduct interdisciplinary systems-based research to improve the resilience, sustainability and profitability of grain production in Western Canada and semi-arid environments in general. Through collaborations with the existing expertise present at the University of Saskatchewan, as well as engagement with grain producers, my program aims to take a broad look at how agronomic practices can be developed or adapted to address sustainability issues. I am particularly interested in testing new systems, for example, cover crops, intercropping, re-integrating livestock with grain cropping, and winter broadleaf cultivation, among others.

Student roles:
The internship will be primarily a field-based position to help with research activities investigating cropping systems and their impact on soil health indicators. Such activities include soil sampling, planting, weed control, plant sample collection, harvest and sample processing (if occurring within the internship period). The intern will be required to keep comprehensive documentation of the research activities. Data analysis might also be possible depending on the timing of the internship. The work may require work under hot sun, and/or windy conditions. Work may also be required on evenings or weekends on occasion when the completion of the work is time-sensitive.

Efforts will be made to take into consideration any activities or training specifically asked by the intern within the context of the internship. This is a great opportunity to experience hands-on agricultural research and understand the demands and rewards of graduate studies and/or a career in research.

Skills required:
We are looking for a motivated and enthusiastic individual that appreciates working outside. The successful candidate should have the ability to work both independently and as a team member, show good organizational skills, follow instructions, ask questions, be able to think critically and overall act professionally in a multicultural context. Skills with Microsoft Office products such as Excel and Word are also necessary.

Undergraduate students in the fields of agronomy, agroecology, soil science, crop science and related fields would benefit to a greater extent from this practical experience. Prior experience with agricultural research would be an asset, but not required.

Research area: Plant genomics and genetics, with a focus on haskap (Lonicera caerulea). The lab conducts genomic studies to identify genetic regions and candidate genes associated with key agronomic traits and environmental adaptation in this emerging berry crop.

Student roles:
The student will assist in phenotyping for key traits under controlled or field conditions; perform data organization and quality control for phenotypic datasets; support initial genomic data processing; annotate candidate genes using publicly available databases; conduct literature searches to identify gene functions related to traits of interest; summarize gene functions and pathways in organized tables; and assist in preparing figures and summaries for manuscripts. Timeline: Month 1 — training in phenotyping protocols, data handling, and introductory genomics tools; begin phenotypic data collection. Month 2 — continued phenotypic data collection and quality control; introduction to gene annotation and literature review. Month 3 — candidate gene identification, integration of results, and preparation of figures and summaries for manuscripts.

Skills required:
Background in plant biology, genetics, or a related life sciences field. Familiarity with basic laboratory techniques and data management is an asset. Interest in genomics, bioinformatics, or plant breeding is desirable. Strong attention to detail and ability to conduct literature searches and synthesize scientific information.

Research area: My research integrates ruminant nutrition, forage management, and environmental sustainability to optimize livestock production systems. I specialize in evaluating alternative feed ingredients and grazing strategies to improve animal performance, health, and economic viability while enhancing soil health, carbon sequestration, and ecosystem services. Current work focuses on bison production in Saskatchewan's mixed-grass prairies, assessing impacts on forage quality, animal behavior, and economics. My approach combines applied animal science with stakeholder engagement, including Indigenous-led conservation and knowledge mobilization for sustainable grazing management.

Student roles:
The student will join an ongoing two-year grazing study comparing continuous and rotational bison production systems at the University of Saskatchewan's Native Hoofstock Centre. Over approximately 10 weeks during the grazing season, the student will work under the supervision of Dr. Eric van Cleef alongside graduate students and technical staff, gaining hands-on experience in integrated livestock systems research.
The student's primary role will focus on field data collection and sample processing, with clearly defined tasks suited to the internship duration. Responsibilities include: assisting with bi-weekly forage biomass sampling using quadrats, conducting botanical composition assessments, collecting forage samples for nutritional quality analysis (dry matter, crude protein, fiber fractions), and supporting soil sampling procedures.
On the animal side, the student will help monitor bison performance by assisting with live weight measurements, observing feeding behavior (grazing time, bite rate, rumination patterns), and supporting post-trial temperament assessments. The intern will also assist with pasture height measurements using rising plate meters and collect fecal samples for parasite monitoring.
Beyond field work, the student will support data entry, sample drying and grinding, and basic laboratory preparations. The student will have opportunities to observe and participate in extension activities, including interactions with producers and Indigenous partners if scheduling aligns with the open grazing demonstration.
This role is designed to provide a focused, immersive research experience in sustainable bison grazing systems. The intern will develop practical skills in livestock handling, forage and soil sampling, animal behavior observation, and data management. No prior research experience is required, but a strong interest in ruminant nutrition, forage management, or sustainable agriculture is essential. The student should be comfortable working outdoors, following safety protocols, and collaborating within a team environment.

Skills required:
The candidate should be an undergraduate student in Animal Science, Agriculture, or a related field with a foundational understanding of ruminant nutrition and forage systems. Livestock handling experience (beef cattle or bison preferred) is an asset. The student should be comfortable working outdoors, collecting field data (e.g., forage sampling, soil collection, animal behavior observations), and using basic computer software for data entry. Strong attention to detail, a willingness to learn, and the ability to work both independently and as part of a team are essential. Interest in sustainable grazing systems and extension activities is desirable.

Research area: Our Lab is a molecular and applied plant pathology laboratory working on several projects about plant pathogens and insect vectors putting at risk the economy in Canada and worldwide. For that we use molecular biology and genomics tools, but we also use classic microbiology and agronomy-based methodologies.

Student roles:
The student will work on insect collection with nets and aspirators. The student will also help to place and collect the traps and will be key on insect identification (something that will be trained for).

Skills required:
The main skill required from the student is curiosity, critical thinking and love for science. Of course, students with a ecology, entomology or biology background will enjoy more the project. This project requires a lot of field work through our beautiful province and also a lot of insect identification and looking through the stereoscope.

Research area: Dr. Juan Francisco Núñez’s research focuses on sustainable supply chains and value chains within agri-food systems, integrating lean and green management principles to enhance efficiency and environmental performance. His work emphasizes lean logistics, supply chain collaboration, and value chain analysis, with particular applications in sustainable agriculture. Through mixed-methods approaches and applied research, he develops strategies to reduce waste, improve operational performance, and foster eco-innovation across production and distribution systems, especially within horticulture and food value chains.

Student roles:
The intern will actively support applied research in Lean Agriculture, working closely with the supervisor and a graduate researcher.
Key tasks include conducting literature reviews (academic and practitioner sources), participating in field visits to agricultural sites, and contributing to data collection and mixed-methods analysis (quantitative and qualitative).
The intern will also assist in preparing field reports and knowledge mobilization materials.
In addition, the student will contribute to continuous improvement initiatives by analyzing and enhancing one or more operational processes at Bishop’s University Educational Farm.

Skills required:
The ideal candidate is a senior undergraduate student (in their final semesters) in sustainable agriculture and food systems, environmental studies, food studies, food production, industrial engineering (with a sustainability focus), or supply chain/logistics with a sustainability orientation.
The candidate should demonstrate a strong interest in sustainable value chains, continuous improvement processes, and applied research.
Fluency in English (reading, writing, and academic communication) is required, while a working knowledge of French is considered an asset.
The candidate must be comfortable working outdoors (e.g., visiting farms) and be motivated, autonomous, and willing to engage in field-based research with agricultural stakeholders.

Research area: Mon domaine de recherche spécialisé est l'écologie forestière, avec un accent sur les interactions entre les sols forestiers, la régénération des essences arborescentes et les pratiques sylvicoles dans le contexte des changements climatiques. Plus spécifiquement, mes travaux portent sur : Les effets des litières et des humus (feuillus vs. résineux) sur la croissance des semis, la disponibilité des nutriments et les propriétés édaphiques ; La biodiversité du sous-bois, incluant les espèces à valeurs ancestrales, médicinales et alimentaires ; La comparaison des peuplements purs et mixtes en termes de productivité ligneuse et de résilience écologique.

Student roles:
Les deux stagiaires participeront à l'ensemble des activités suivantes : collecte des sols et des humus sur le terrain, tamisage et préparation des substrats, lavage délicat des racines des semis, transplantation des plants dans les différents traitements, préparation des échantillons de sol pour analyses physico-chimiques (pH, C:N, nutriments, etc.), mesures régulières de la croissance des plants (hauteur, diamètre, biomasse), suivi hebdomadaire du pH des substrats, et arrosage quotidien des semis en conditions contrôlées.
L'objectif est de comprendre comment l'héritage édaphique post-coupe et les apports de litière influencent la régénération forestière, afin d'identifier les combinaisons humus-litière permettant d'optimiser la croissance des semis sans recours aux fertilisants chimiques.

Skills required:
Nous recherchons deux stagiaires avec une formation en foresterie, biologie, écologie, agronomie ou techniques de laboratoire. Les compétences requises incluent : rigueur pour le tamisage, la préparation des substrats, le lavage des racines (délicat) et la transplantation; capacité à effectuer des mesures régulières (hauteur, diamètre, biomasse); autonomie pour l'arrosage quotidien et le suivi hebdomadaire du pH. Le travail en serre exige de la minutie et une bonne condition physique pour la collecte terrain. Un français fonctionnel est fortement recommandé (Edmundston est francophone, mais je parle portugais et anglais). L'anglais, l'espagnol ou le portugais sont acceptés avec une motivation FR.

Research area: My research examines relationships among people, soil, and ecosystems using transdisciplinary approaches that combine environmental communication, agroecology, geography, soil science, and community-engaged research. I am particularly interested in how social and ecological relationships persist through time and shape present-day landscapes and human-soil relations. My current work investigates soil stewardship, ecological memory, and social-ecological systems in collaboration with farmers, Indigenous communities, artists, and scientists. Across these projects, I ask what social and environmental logics and infrastructures support or impede communities in sustaining the conditions for collective life.

Student roles:
The student will work as part of a field research team contributing to the Soil Memory Atlas based at Klondike Valley Nursery near Dawson City, Yukon. Accordingly, they will participate in a combination of field-based research, environmental documentation, and participatory agroecological fieldwork. Activities may include ecological observations, photography, interviews, informal conversations with site stewards, archival research, review of historical photographs and maps, and documentation of stewardship practices across the landscape.

A central component of the internship involves identifying and documenting "traces"—observable indicators of ecological processes, historical events, stewardship activities, and social relationships that continue to shape present-day conditions. The student will maintain detailed field notes, organize research materials, contribute to trace documentation, and prepare short analytic reflections on emerging patterns and findings.

The internship also includes participation in selected orchard, nursery, and garden activities. Through direct involvement in soil stewardship and food production, the student will gain first-hand experience of how ecological knowledge is generated through ongoing care of landscapes. These activities form an important part of the research process and provide context for interpreting observations gathered through interviews, historical materials, and ecological monitoring.

Throughout the internship, the student will participate in regular research meetings, contribute to synthesis discussions, and collaborate with other members of the research team. Depending on their interests and experience, they may also contribute to mapping, digital storytelling, visual communication, or Atlas development activities.

The internship will culminate in a contribution to the Soil Memory Atlas, which may take the form of a digital atlas entry, story map, research report, visual documentation project, or presentation summarizing key findings from the field season.

Skills required:
Applicants should have an interest in environmental studies, geography, agriculture, ecology, anthropology, communication, history, soil science, or related fields. Previous experience conducting research, fieldwork, or community-engaged projects is an asset but not required. Strong observational skills, curiosity, adaptability, and a willingness to learn are essential. Students should be comfortable working both independently and collaboratively in a small team and participating in outdoor field activities. The internship takes place in a remote northern environment with canoe access only; applicants should be enthusiastic about field-based research, adaptable to changing conditions, and comfortable living and working in a small community setting.

Research area: Research in the Zhang lab focuses on the characterization of agrifood nutrients and bioactivities using diverse analytical techniques. Examples include liquid chromatography (LC), gas chromatography (GC), and their combination with mass spectrometry (MS). We also study how those bioactive components affect human health (or disease progression). Some of the existing research projects in this research group include food macro- and micro-nutrient measurement and their changes during food processing, food flavor and aroma compound characterization, and non-invasive biomarker screening for Parkinson’s Disease.

Student roles:
Under the guidance of the supervisor, the student will be trained to work independently, to perform LC-MS method development, vitamins A and D extraction from food, LC-MS instrument operation, data collection, analysis, and reporting. From this training, the student will gain hands-on experience of the latest analytical technology, LC-MS, learn related software (for instrument operation and data processing), and good laboratory skills. The student is required to write clear laboratory notebook, summarize the results and report to the supervisor on a regular basis.

Skills required:
The desired candidate should have a good background in analytical chemistry, biochemistry, or biology, have basic knowledge on chemical solution preparation, and good pipetting skills. Good laboratory practice, attention to details and communication skills are preferred.

Research area: The Sustainable Agricultural Landscapes (SAL) Lab is dedicated to providing science that contributes to understanding the ecology of and management for an agricultural system that meets current needs without comprising the needs of future generations. While sustainable agriculture should ensure that numerous needs are met, including those that are social and economic in nature, the SAL Lab focuses specifically on those related to the environment.

Student roles:
The student will receive an initial orientation to the project, research objectives, laboratory procedures, and analytical methods from the laboratory Principal Investigator. Following this orientation, the student will work closely with a project researcher and be part of a collaborative laboratory team that provides day-to-day guidance, mentorship, and feedback.

The student will be involved in preparing and processing soil samples collected from agricultural regions across Canada and will receive training in Mid-Infrared (MIR) spectroscopy and associated laboratory techniques. Working alongside researchers, the student will contribute to the development and evaluation of predictive models used to estimate soil health indicators and support digital soil mapping initiatives. As part of this work, the student will gain experience with data management, quality assurance and quality control procedures, statistical analysis, and interpretation of environmental datasets.

The student will be encouraged to work collaboratively with project researchers to troubleshoot challenges, develop solutions, and gain a deeper understanding of how laboratory data are transformed into tools that support sustainable agricultural management. Opportunities may also exist to participate in complementary laboratory activities related to soil and plant analysis.

As part of a national research collaboration, the student will gain exposure to the broader scientific process and the application of emerging technologies to environmental monitoring and sustainability challenges. The student will also be invited to participate in regular laboratory meetings, seminars, and discussions, providing opportunities to learn about a diverse range of ongoing research projects in soil health, climate change mitigation, and sustainable agriculture.

The technical, analytical, and research skills developed through this internship will provide excellent preparation for future careers or graduate studies in environmental science, agriculture, sustainability, data science, and related fields. Upon successful completion of the internship, project supervisors will be pleased to provide letters of reference and support.

Skills required:
Successful applicants will possess strong organizational skills and attention to detail. A willingness and ability to problem solve and think critically is essential, as is demonstrated previous experience with field and laboratory work. Applicants' resumes should include a summary of field and laboratory course work completed and any applicable employment or volunteer experience. The applicant must be willing to work outside in adverse weather and lift 20 kg.

Research area: This research lab specializes in perennial fruit crops and their wild relatives. We work at the intersection of plant agriculture and data analytics to quantify and characterize trait and genomic variation. Ongoing work includes projects on grapes, strawberries, blueberries, and apples, some of the most important fruit crops in both Canada and globally. We use computational tools to improve our understanding of fundamental plant biology and provide evidence-based recommendations for plant breeding, management, and conservation strategies.

Student roles:
The student will contribute to an image-based analysis of morphological traits in a perennial fruit crop species. The student will begin by performing a review of the literature for relevant scientific articles including any previous work on morphological variation in the selected species.

During this time, the student will begin to process the image datasets, which will consist of either scanned leaves or photographed fruit. These images will need to be annotated and prepared for downstream statistical analysis. Next, using a combination of R and Python and following documented protocols in the research lab, the student will extract morphological measurements for comparisons both within and across species.

After morphological data have been collected, the student will use data analytics to investigate morphological variation. This step will involve integrating additional information, such as species or environmental information, to determine if morphological variation is correlated with any factors of interest. The student will be responsible for data visualization and analysis.
Throughout the project, the student will document their workflow using reproducible research practices. At the end of the term, they will prepare a final report summarizing their results and conclusions and organize all image annotations and code for future use.

Skills required:
The student should be experienced with the R programming language and reproducible research practices. Knowledge of ImageJ or python would be considered an asset. The student is expected to have some experience or interest in plant biology/botany. The student is also expected to be familiar with scientific literature review to place the results of their work into a broader context. The student should be a strong writer and work well both independently and as a part of a team. There is some repetitive work during data collection and the student should be organized, detail-oriented, and have strong time management skills.

Research area: Our research group specializes in fruit genomics, horticultural phenomics, and quantitative genetics. This project sits at the interface of field-based trait measurement and genomic data analysis, leveraging whole-genome sequencing of apple accessions to dissect the genetic architecture of agronomic and fruit quality traits relevant to cultivar improvement on the Canadian Prairies.

Student roles:
The summer intern should be able to record the agronomic data, perform fruit quality analysis and conduct genome wide association studies on apple germplasm collections at USASK. Proper guidance is provided to the intern. They should operate and calibrate instruments such as refractometer, titration apparatus, and colour meter. They should conduct regular orchard assessments of vegetative phenology and canopy traits across all accessions. The intern should also be curious to learn and perform phenotypic data organization and conduct genome wide association studies and statistical analyses; and finally produce summary figures and contribute to a peer-reviewed publication.

Skills required:
The ideal candidate should be enrolled in degree program at agriculture related field. They should have experience or strong interest in laboratory analytical techniques and attention to measurement precision. The candidate should be comfortable with outdoor fieldwork for periodic orchard visits during summer conditions and should possess basic data handling skills in excel.

Research area: My group works on pulse crop genetics and genomics as well as running a common bean breeding program. Our primary focus is on lentil genetics and we have many well characterized populations for which we are developing deep datasets for genetic analyses. While conducting large genomics projects we sometimes identify curiosities that we would like to investigate further and are ideal for an internship student.

Student roles:
The Globalink student on this project will evaluate the ability of different lentil breeding lines to generate multiple shoots under controlled conditions.
Week 1: Introduction to the team, facilities, and our projects. The student will complete the required safety courses. The student will begin a literature review on the development of lentils and plant tissue culture.
Week 2-7: The student will conduct a series of germination assays on agar Petri plates using 100 lentil lines exposed to different chemical compounds at varying concentrations and lengths of time.
8-10: A germination assay will be conducted within soil filled pots to validate the findings from the agar plate study in more realistic growing media.
Week 11-12: The student will analyse data and generate a final report to present their findings and experience to the members of our team.
The student will work closely with a PhD candidate well-versed in lentil germination methodology. Monthly (or more frequently as required) in-person meetings will be held between the student, supervisor, and the PhD candidate.

Skills required:
The student should have background knowledge in basic botany and genetics. The student must be curious, with a desire to learn and ask questions. The student must be able to speak, read, and write in English. The student must be able to work both independently and as part of a team. Previous experience in a laboratory working with hazardous chemicals or plant tissue culture would be beneficial but are not necessary.

Research area: The researcher is a trained chemist and environmental engineer with almost 10 years of experience in various fields comprising value-addition of waste biomass into various bioproducts by submerged and solid-state fermentation comprising enzymes, biopesticides, organic acids, bioenergy, polyphenolic compounds, animal feed among others. She has worked on the complete process comprising upstream and dosnstream processing techniques until the application stage as formulations as well as techno-economic analysis of the process. The researcher has expertise in the field of emerging contaminants and its by-products in complex media, such as wastewater, wastewater sludge, soil and biosolids.

Student roles:
The student will be responsible for following tasks in the present project:
1. Physical-chemical analysis of the agro-industrial wastes.
2. Optimization of process conditions of ligninolytic enzymes production using fungal monocultures .
3. Optimization of process conditions in flask scale experiments using response surface statistical approach.
4. development fo BEMS/BENS.
5. Correlation of data with literature publications on removal of pharmaceutical compounds.
6. Compilation of results.
7. Report preparation.

Skills required:
The student will be required to have a general chemical/biochemical engineering/biotechnology background as the research project involves bioproduction of enzymes using agro-wastes by solid state and liquid fermentation. In addition, the student must be good in statistical analysis to verify the reproducibility of concentrations of enzymes and also chemistry skills for analysis of emerging contaminants. Basics in process control will be an asset. The project requires basic microbiology understanding for inoculation, petri plating etc.

Research area: Dr. Michelle Carkner studies how farming systems can work with nature rather than against it. Her research asks: what happens when we design crop rotations that mimic natural ecosystems with diverse plants, grazing animals, and no synthetic inputs? Working on the Canadian Prairies, she combines long-term field experiments, plant ecology, and farmer-led plant breeding to understand how crop diversity supports soil health, nutrient cycling, and resilience to climate stress. From Manitoba to Zimbabwe, her work connects laboratory science with real farms and real farmers, asking not just whether alternative agriculture works, but how and why it works.

Student roles:
The intern will conduct weekly nitrous oxide flux measurements using closed chamber techniques across replicated field plots, maintain accurate field records, assist with sample processing, and perform basic statistical analysis of flux data. Alongside the primary project, the intern will participate in seeding, biomass sampling, and other field activities across the lab's broader research program, providing hands-on exposure to Canadian cropping systems agronomy. The intern will work closely with the research team and will receive full training on all protocols. This is an excellent opportunity to gain a well-rounded education in Prairie field crop research.

Skills required:
The student will conduct weekly nitrous oxide (N₂O) flux measurements using round-top closed chambers across organic and conventional crop rotation plots at the Glenlea Long-Term Rotation site in southern Manitoba. Training will be provided on chamber deployment and gas sampling protocols. The student must be comfortable working outdoors in variable Prairie weather conditions and able to work independently in the field. Strong attention to detail is essential, as precise and consistent sampling technique directly affects data quality. A background in soil science, agronomy, or environmental science is an asset, as is familiarity with basic data recording and spreadsheet management.

Research area: My research focuses on operations research and supply chain management, with an emphasis on vehicle routing, stochastic optimization, and humanitarian and emergency logistics. I develop mixed-integer programming formulations and matheuristics for problems involving uncertainty, multiple vehicle types, and time-sensitive constraints. Recent work spans agrifood logistics, fleet management, and resilience planning, often in collaboration with public and cooperative organizations in Québec. I am particularly interested in models that remain computationally tractable while capturing the operational realities faced by decision-makers in the field, including risk, capacity, and service-level trade-offs.

Student roles:
The student will be expected to: conduct a literature review on patrol routing, arc routing, and multi-vehicle/multi-crew routing problems; formulate one or more mathematical models (mixed-integer programming and/or heuristic/matheuristic approaches) for the two-crew patrol problem; implement the models and design or adapt benchmark/realistic instances representative of a wildfire-prone region; run computational experiments, analyze trade-offs (coverage vs. response time vs. cost), and validate results; document the methodology and findings in a written report or thesis chapter, with the goal of producing a manuscript suitable for submission to a peer-reviewed journal; and present progress regularly in research group meetings.

Skills required:
Strong background in operations research, applied mathematics, or industrial/computer engineering. Solid programming skills (Python or Julia) and familiarity with optimization solvers (Gurobi, CPLEX, or open-source equivalents) are required. Prior coursework or experience in vehicle routing, integer programming, or metaheuristics is a strong asset. Comfort with mathematical modeling and the ability to read and synthesize academic literature are essential. Interest in environmental or public-sector applications is valued but not mandatory. Good written English or French is required for eventual publication.

Research area: My research specialization includes agricultural data science. This combines my understanding of agronomy, the science of crop production, with expertise in data analysis. I can analyze vast datasets collected from smart agriculture practices like sensors and weather stations. By looking at factors like soil conditions, plant health, and historical yields, I can help develop data-driven strategies for optimizing irrigation, fertilization, and pest control. Ultimately, my goal is to use data science to improve crop production efficiency and sustainability.

Student roles:
The student will assist the project by involving in the literature review. The student will focus on assisting with secondary data collection, analysis, and field experiments. The student will also contribute to data organization, quality control, and basic analysis tasks. Additionally, the student will assist in field experiments by collecting data, monitoring implementation, and providing support to farmers. This hands-on experience will provide valuable insights into data-driven smart agriculture practices and their potential to improve crop production. The student will also gain experience in teamwork, communication, and the practical application of research in real-world agricultural settings. Finally, the student will draft sections of a final report documenting their internship experience, the methods used, and key findings, all under the guidance of the project leader.

Skills required:
The student must have a basic understanding of smart agriculture technologies and precision farming techniques, including experience in areas such as IoT sensors, remote sensing, data analytics, and artificial intelligence. The candidate should also demonstrate problem-solving and critical thinking skills to identify opportunities for improving crop production and resource efficiency through data-driven decision-making. Strong communication and collaboration abilities to work effectively with farmers, policymakers, and stakeholders are also required.

Research area: My research program investigates integrated forage and grazing systems to improve the productivity, resilience, and sustainability of livestock agriculture. I study forage agronomy, plant–animal interactions, nutrient dynamics, biological nitrogen fixation, pasture biodiversity, and grazing management practices that optimize forage utilization, animal performance, and ecosystem services across diverse production environments.

Student roles:
The student will assist with field and laboratory research activities related to forage and pasture systems. Responsibilities will include collecting plant and soil samples, assessing botanical composition and forage productivity, recording and organizing data, and supporting sample processing and analysis. The student will participate in data management, preliminary statistical analyses, and interpretation of results under supervision. They will work closely with graduate students and research staff, gain experience in experimental design and scientific methods, and contribute to research aimed at improving the productivity and sustainability of forage-based livestock production systems in Saskatchewan.

Skills required:
The ideal candidate is an undergraduate student pursuing studies in Agronomy, Plant Science, Animal Science, or a related field. Students should have a strong interest in forage and pasture research, sustainable livestock production, and agricultural systems. Prior experience with field research, plant identification, data collection, or laboratory analyses is beneficial but not required. The successful candidate should be willing to work outdoors under field conditions as well as in laboratory, demonstrate attention to detail when collecting and recording data, and possess strong organizational and problem-solving skills. Basic knowledge of plant biology, statistics, and data management is desirable.

Research area: My research program aims to develop and apply advanced analytical chemistry technologies to solve challenging and emerging issues faced by the agri-food industry. Specifically, my research has been focusing on the advancement in novel and reliable chemo- and bio-sensor- and instrument-based analyses for food chemical and biological hazards as well as food adulterants; development and implement point-of-need microfluidic “lab-on-a-chip” devices to achieve real-time, cost-effective and high-throughput analysis; and development and application of mass spectroscopic based metabolomics and bioinformatics to systematically investigate food products, including their safety and quality properties.

Student roles:
The student will work together with a senior student in my lab for performing literature review and experiments including optimizing sample preparation procedures, preparing samples for mass spectrometry analysis, as well as processing and interpreting data collected. If time permits, the student will also help with collecting agri-food samples to generate PFAS profile in local agri-food systems.

In addition to perform the research work listed above, the student will also need to attend weekly group meeting and present results during the group meeting.

Skills required:
Students from agricultural science, biochemistry, food science, chemistry and related areas are welcome. The student is expected to have bench work and data analysis experiences in analytical chemistry, preferably in mass spectrometry, vibrational spectroscopy (e.g., Raman and IR spectroscopy), and sample preparation using SPE, SPME and basic liquid-liquid extraction. The student should possess literacy skills, including searching, reading and interpreting scientific literatures relevant to the research topic. Other general skills that are expected include self-learning skill, teamwork skills and communications skills (both written and verbal in English).

Research area: My specialized research area focuses on computational agriculture, specifically the development of a high-throughput image-based phenotyping pipeline to evaluate plant growth and performance under variable soil conditions. The work integrates digital imaging, data analysis, and plant science to assess how soil physicochemical properties influence the phenotypic traits and adaptability of haskap (Lonicera caerulea) cultivars.

Student roles:
for Canadian haskap genotypes, with a focus on proliferation and shoot multiplication. Their role will include preparing culture media, maintaining sterile conditions, initiating and subculturing in vitro plant materials, and testing different plant growth regulator treatments to improve regeneration efficiency. The student will also record and analyze growth responses, maintain proper documentation of experimental data, and support routine laboratory activities. Overall, they will contribute to developing a reliable micropropagation system that can produce high-quality plantlets suitable for distribution to farmers and commercial cultivation.

Skills required:
The student should have a background in plant sciences, agriculture, biotechnology, or a related field, with a strong interest in plant tissue culture and micropropagation techniques. Prior laboratory experience in sterile techniques, media preparation, and basic plant in vitro handling would be highly beneficial. Familiarity with plant growth regulators and an understanding of plant physiology will be an advantage. The candidate should also demonstrate good attention to detail, strong organizational skills, and the ability to work independently as well as in a research team setting.

Research area: My research fields concern industrial ecology and climate change mitigation. My work is also focused on determining the effects of climate change on the biogeochemistry and soil-atmosphere gas exchanges in agriculture and from boreal and subarctic ecosystems.

Student roles:
The candidate will take part in all the works related to the projects in industrial ecology: greenhouse gas measurements on the field, soil sampling and related treatments, sample processing in laboratory, measurements of vegetation productivity indices, data compilation, learning the basics of industrial ecology and on the science of climate change. The successful candidate will be based at the Chair on eco-advising at the Université du Québec à Chicoutimi. This traineeship also gives the opportunity to realize a research project within the framework of a course given in the study program of the candidate.

Skills required:
-Following a study program in biology, environment, chemistry, physic or any other
relevant programs related to the field
-Clear interest for industrial ecology, issues related to climate change and greenhouse gas management
-Capacity to integrate a multidisciplinary research group at the Chair on eco-advising
-Capacity to do physical and meticulous work on the field and in laboratory

Research area: Research area: sustainable agriculture, organic farming, biodiversity It is well understood that increasing biodiversity within the field and throughout the landscape is key for optimizing productivity, sustainability and resilience in agricultural production. However, in order to determine the most effective and practical approaches to achieve this, novel research is required. This research project examines the potential of increasing biodiversity throughout agricultural landscapes with the use of biodiverse hedgerows. Biodiverse hedgerows are strips of land dedicated to a diversity of trees, shrubs and perennial herbaceous species. They offer many ecosystems services which benefit the production system.

Student roles:
The student will be under the direct supervision of Dr. Jane Morrison and will spend their time working both in the field and in the lab. The student will be working alongside MSc students. The student will assist in fieldwork tasks such as: planting, pruning, weed management, soil sampling, data collection (e.g., visual observations of insects, soil moisture, plant measurements), and other related tasks. The student will be expected to work in various types of weather conditions and perform physical work.

The student will also perform lab and office work including: material preparation, insect sorting, data entry, simple analyses, etc.

The student will spend a great deal of time at the Bishop's Educational Farm, a living land laboratory where our faculty and staff aim to instill passion for learning, drive agroecological science forward, and foster transformational change towards resilience and sustainability in our food system. The student will be working on one of the many research projects that take place on the farm.

Skills required:
The student should be hardworking, disciplined, and punctual. The student should be organized and able to follow instructions and take careful measurements. The student should be able and willing to do physical work in various types of weather conditions. The student should have a strong interest in sustainable agriculture and food systems and be keen to contribute to related research and gain research skills. Having taken some classes related to agriculture, sustainability, soil science, ecology, or other related fields would be considered an asset. Having some farm experience would also be an asset.

Research area: The Advanced Materials for Sustainable Energy Technologies Laboratory (AM-SET-Lab) develops new materials and enhanced technologies for applications in carbon capture, thermal energy storage, thermophotovoltaics, and solar energy harvesting and microclimatic control of lighting, heating, cooling and ventilation in buildings and greenhouses. The AM-SET-Lab is also pursuing the integration of Life Cycle Assessment (LCA) techniques to inform and direct its research objectives.

Student roles:
The Mitacs students will work closely with graduate students within the AM-SET-Lab, and will help conduct breakthrough adsorption and desorption measurments to determine the amount of carbon dioxide captured and released from different adsorbents. A unique aspect of this project is that a solar simulator will be used to provide light energy to drive the carbon capture process. The Mitacs students will be trained to characterize the structural and optical properties of these adsorbents using techniques such as UV-Vis and FTIR spectroscopy. The student will also help to design adsorbent bed configurations that enable incident solar-simulated light to be adsorbed with high efficiency. Furthermore, life cycle assessment and life cycle costing techniques will be taught to the student, and they will conduct a techno-enviro-economic assessment to compare the performance of solar and electrothermally driven carbon capture to other methods (such as using electric power, heat pumps, or waste heat to capture carbon). Different scenarios, such as point source capture from high concentratoin sources, building intergrated carbon capture, and direct air capture will be considered.

Skills required:
Students should have previous experience with experimental methods, instrumentation, and laboratory practice. Students must be able to conduct experiments, paying close attention to experimental conditions including lighting, humidity, pressure, flow rates, and temperature profiles throughout the testing apparatus. Students should have a strong background in thermodynamics and heat transfer, and knowledge of materials science is an important asset. Students should have good communication skills, a strong work ethic and a willingness to work efficiently as a team member.

Research area: Our team works mostly on plant aroma and how to create new cultivars with a unique flavor. We also work on the factors controlling flowering, pigmentation, and disease resistance—essential aspects in breeding horticultural crops. Our current projects focus on strawberries, potatoes, and tomatoes. We use genetic and biochemistry tools to better understand plants biology. Our work also includes field studies where we evaluate new lines from our breeding programs. Key words: Horticultural science, plant volatiles, aroma, flavor, flowering, disease resistance, breeding, agriculture, biology, biochemistry, plant genetics.

Student roles:
The student will be part of a scientific team including research associates and graduate students. He will have the occasion of learning from the expertise of several lab members. He will be working closely with a graduate student, offering a good opportunity to learn from him. The student will have his own little project with objectives, but he will also learn different techniques that we often use in the lab. Among these, are:
- Molecular biology (DNA extraction, PCR, CRISPR, etc.)
- Genotyping (HRM)
- Plant volatiles analysis (gaz chromatography)
- Managing field experimental plots
- Plant tissue culture
- Genomic data analysis (GBS, RNAseq, bioinformatic)

The student will be provided scientific papers to learn on his subject and will be encouraged to explore by himself the latest literature to discuss with the other team members about the future of the field. He will analyze the data of his project and will discuss with the team about where the project could or should go after. The lab has several grants that can provide future opportunities for graduate studies.

Skills required:
The student must have sufficient background knowledge in plant biology and genetics. Programs in agriculture, biology or biochemistry would probably provide a good background for the project but other disciplines could be appropriate. Basic lab techniques experience would be an asset (e.g. molecular biology).

Research area: The use of fast growing and high-yielding plantations is relatively new in Canada, due to the presence of vast natural forests. However, the always increasing demand for wood products, social preoccupations for native forests and the slow growth rates of these forests make intensively managed fast growing plantations increasingly interesting. For more than 20 years, we have established hybrid poplar plantations all over the region in order to develop silvicultural expertise and obtain productivity data.

Student roles:
The student will be responsible for gathering the data in the field (with the help of a technician), and compilation of data.
Next he/she will analyse the data to procude productivity tables and write a report and/or scientific publication.

Skills required:
The student should have basic knowledge in dendrometry of trees.
Must like working outside and have a basic understanding of statistical analyses.

Research area: My research focuses on farm animal welfare, behaviour, management and health. The aim is to improve welfare and health through effective and applied on-farm research projects, working together with the producers to improve their daily management practices and along with their own welfare. Currently, we are working with dairy animals, calves and cows, on projects looking at welfare and health during their entire lifespan, from birth to slaughter, with a focus on hoof health.

Student roles:
The student’s main role will be to participate in data collection on farm for different project related to hoof health. Based on the individual student abilities, experience and interest, they will be given a piece of the data collected to analyze ensuring they each have their own little indivudual project. They will be in charge of conducting the analysis and report the results in writing for their individual project. Depending on the interest and abilities of the candidate, they may also participate in the writing of a scientific manuscript regarding the results of their statistical analysis and participate in data collection for other projects going on in the lab.

Skills required:
Required skills/background for the student are having interest in animal science/production, health and welfare. Ideally, the student will have abilities, interest and experience with on-farm data collection and in statistical analysis. The student must be willing to take an active part of multidisciplinary work team and therefore must be able to easily communicate in English or in French. The ideal student would also have an interest in working closely with a variety of member of the dairy industry. Knowledge in dairy production is an asset.

Research area: Forages, Rangelands, Soil C Sequestration, Soil Health, Grazing, Greenhouse Gas Emissions.

Student roles:
Students will assist in collecting and analyzing soil and greenhouse gas (GHG) samples across multiple on-farm sites throughout Alberta. The students will run tests of soil activity, C and N mineralization, and estimate particulate and mineral associate C fractions on soil samples were collected in 2024 (before treatment) and are being collected again in 2026 (after treatment). Furthermore, students will help on ongoing activities, such as vegetation monitoring and GHG flux measurements (CO2, CH4, and N2O) in the ranches.

Soil biological activity overview:
Soil samples collected from pasture will be processed and subjected to a controlled laboratory incubation to assess microbial activity and the decomposition of soil organic matter. Carbon mineralization will be estimated by measuring carbon dioxide (CO2) released during the incubation period, while microbial biomass C will be determined using a fumigation-incubation approach. Additional analyses will conducted to quantify total soil C and N concentrations, soil organic matter, and soil C and N stocks. Soil inorganic nitrogen (ammonium and nitrate) will also measured before and after incubation to estimate N mineralization and nitrification rates.

Organic matter fractionation overview:
Soil organic matter will separated into particulate organic matter (POM) and mineral-associated organic matter (MAOM) fractions using a physical fractionation procedure. Soil samples will be dispersed in a sodium hexametaphosphate solution, sieved, and separated based on particle size. The material retained on the sieve is classified as the POM fraction, while the material passing through the sieve is classified as the MAOM fraction. The POM and MAOM fractions will then be finely ground and analyzed for C and N concentrations. These measurements reflect the distribution of organic matter between labile and more stable soil C pools, providing insight into soil C storage, nutrient cycling, and the long-term stabilization of organic matter in soil.

Skills required:
Background in agriculture, agronomy, crop science, rangelands, animal sciences, soil science, environmental sciences and related areas.

Research area: I am an agronomist and crop physiologist at the University of Guelph. Our team of diverse research staff and graduate and undergraduate students have expertise in fieldwork, agronomy, crop modelling, and data analysis. The goal of the research program is to help farmers improve yields, increase profitability and reduce the environmental impacts of agricultural production.

Student roles:
The MITACS student will be trained in all methods and techniques by professors, graduate students and/or research technicians. It is expected that as the MITACS intern gains experience, they will be able to perform tasks with more and more independence:

1) Measure germination and emergence of corn and soybean varieties in field and laboratory conditions.
2) Measure soil properties such as bulk density, aggregate sizing, soil texture.
3) Take soil and crop measurements periodically throughout the season (working with skilled technicians and graduate students).
4) Collect and organize data in spreadsheets. Ability to conduct data cleaning and data analysis will be looked on favorably.
4) Attend conferences to learn more about Ontario agriculture. Join the research team in presenting research results to farmers, policymakers, and other scientists.

Skills required:
We are seeking a motivated intern who is excited to visit Canada, learn about Ontario's agriculture, and conduct rigorous scientific agronomic research. The student must enjoy working as part of a team. The exact nature of the internship will be tailored to the successful candidate's skills, interests and expertise. For example, more (or less) emphasis can be placed on lab work or data analysis depending on student interest.

At minium, the intern should be comfortable working outdoors at research station farms and on commercial farm fields.

Research area: Je suis professeure en foresterie urbaine, verdissement urbain, sols urbains, physiologie des arbres et interactions sol–plante. Mes travaux sont transdisciplinaires et visent à développer des outils novateurs pour mieux intégrer les arbres en ville. En tant qu’ingénieure forestière, je relie science et pratique en combinant terrain, laboratoire et approches participatives. À travers la Chaire de recherche sur l’arbre urbain et son milieu, je collabore avec des partenaires municipaux et d'autres partenaires pour relever des défis liés à la biodiversité, aux changements climatiques et aux services écosystémiques, tout en contribuant à la formation de personnel hautement qualifié.

Student roles:
La personne stagiaire travaillera à la fois en laboratoire et sur le terrain, où elle contribuera à la collecte de données sur les arbres et la biodiversité environnante en milieu urbain. Cette personne apportera un soutien aux membres du personnel de recherche ainsi qu’aux autres personnes étudiantes impliquées dans ce projet et dans des projets connexes portant sur les arbres en milieux privés à Québec.
La personne stagiaire contribuera directement à la collecte de données sur les arbres privés, en réalisant des inventaires détaillés et des évaluations de la santé des arbres selon des protocoles standards en foresterie et en arboriculture. La participation au traitement, à l’organisation et à la validation des données collectées fera également partie des responsabilités.
Une formation approfondie sur les méthodes d’inventaire des arbres, l’identification des espèces urbaines et le traitement des données sera offerte. La participation aux communications avec les personnes participantes au projet et aux activités de recrutement pour encourager la collecte de données sur les arbres est également prévue.

Skills required:
Les compétences et le profil recherchés incluent une capacité à travailler en équipe, une attitude proactive et une forte motivation à apprendre. Une formation en agriculture, écologie, biologie ou dans un domaine connexe est souhaitée, ainsi qu’un intérêt pour la foresterie urbaine et le verdissement urbain. Une expérience en travail de terrain ou en collecte de données constitue un atout. La personne candidate doit être en mesure de communiquer en français; une capacité à comprendre ou à s’exprimer en anglais est également souhaitée. Rigueur, organisation et intérêt pour le travail collaboratif sont essentiels.

Research area: I study the reaction between water, rocks, and gases on Earth and Mars using laboratory experiments, field measurements, and numerical models.

Student roles:
The student will work in a laboratory setting performing experiments or analyses related to life's origins.

Skills required:
The student would need a basic understanding of chemistry and a willingness to work in the laboratory, performing experiments and on a computer, analyzing data.

Research area: My research focuses on plant–soil microbiome, ecology, soil biodiversity, and ecosystem resilience across agricultural, forest, and urban ecosystems. The Plant–Soil Microbiome Ecology and Innovation Lab at UBC integrates ecology, soil science, plant science, microbial ecology, molecular biology, environmental DNA, bioinformatics, computational approaches, and spatial analysis to understand how soil and root-associated microbiomes contribute to plant health, nutrient cycling, ecosystem recovery, and climate adaptation.

Student roles:
The intern will assist with setting up and maintaining soil microcosms, applying moisture treatments, recording experimental observations, processing soil samples, organizing data, reading relevant literature, participating in lab discussions, and preparing a final report and presentation. The student will work under supervision and as part of a team that includes graduate students and/or a postdoctoral researcher.

Skills required:
The student should have a background or strong interest in biology, ecology, environmental science, microbiology, soil science, plant science, agriculture, or a related field. Basic laboratory experience, attention to detail, and comfort working with experimental protocols are important. Experience with soil measurements, microbial ecology, spreadsheets, R, or basic statistics is an asset but not required.

Research area: My research focuses on plant–soil microbiome, ecology, soil biodiversity, and ecosystem resilience across agricultural, forest, and urban ecosystems. The Plant–Soil Microbiome Ecology and Innovation Lab at UBC integrates ecology, soil science, plant science, microbial ecology, molecular biology, environmental DNA, bioinformatics, computational approaches, and spatial analysis to understand how soil and root-associated microbiomes contribute to plant health, nutrient cycling, ecosystem recovery, and climate adaptation.

Student roles:
The intern will support field sampling at UBC Farm or related sites, record site and management metadata, process soil samples, maintain a sample inventory, read and summarize literature on agroecosystem soil microbiomes, assist with preliminary data summaries, participate in lab meetings, and prepare a short final report and presentation.

Skills required:
The student should have a background or strong interest in agriculture, soil science, ecology, microbiology, environmental science, plant science, or a related field. Field and laboratory experience are assets but not required. Familiarity with spreadsheets, basic statistics, R, GIS, or microbiome data analysis would be helpful. The student should be comfortable working outdoors and in a supervised laboratory environment.

Research area: My research focuses on plant–soil microbiome, ecology, soil biodiversity, and ecosystem resilience across agricultural, forest, and urban ecosystems. The Plant–Soil Microbiome Ecology and Innovation Lab at UBC integrates field ecology, soil science, plant science, microbial ecology, molecular biology, environmental DNA, bioinformatics, computational approaches, and spatial analysis to understand how soil and root-associated microbiomes contribute to plant health, nutrient cycling, ecosystem recovery, and climate adaptation.

Student roles:
The intern will assist with field sampling of forest soils and root-zone materials, record site and tree-level metadata, process samples in the laboratory, help maintain a sample inventory, read and summarize relevant scientific literature, organize data tables, participate in lab meetings, and prepare a short final report and presentation. The student will work closely with the supervisor, graduate students, and/or postdoctoral researcher as part of a collaborative field and laboratory team.

Skills required:
The student should have a background or strong interest in ecology, forestry, biology, environmental science, soil science, microbiology, plant science, or a related field. Previous field experience, basic laboratory skills, familiarity with spreadsheets, and interest in microbial ecology are assets. Prior experience with R, GIS, or microbiome data analysis is helpful but not required. The student should be willing to work outdoors as part of a supervised field crew and follow safety protocols for field and laboratory work.

Research area: Dr. Michelle Carkner studies how farming systems can work with nature rather than against it. Her research asks: what happens when we design crop rotations that mimic natural ecosystems with diverse plants, grazing animals, and no synthetic inputs? Working on the Canadian Prairies, she combines long-term field experiments, plant ecology, and farmer-led plant breeding to understand how crop diversity supports soil health, nutrient cycling, and resilience to climate stress. From Manitoba to Zimbabwe, her work connects laboratory science with real farms and real farmers, asking not just whether alternative agriculture works, but how and why it works.

Student roles:
We are seeking a motivated undergraduate research assistant with a background in agriculture, plant science, environmental science, or a related field. The ideal candidate is comfortable working outdoors in variable field conditions and able to perform physical tasks such as installing chambers and collecting plant and soil samples. Attention to detail and careful record-keeping are essential for accurate data collection. Familiarity with basic plant identification (crops and weeds) is an asset, as is experience with data entry or spreadsheets. Above all, we value reliability, curiosity, and a willingness to learn. No prior research experience is required, just enthusiasm for hands-on science.

Skills required:
We're seeking an undergraduate research assistant with a background in plant science, agronomy, environmental science, or a related field. The ideal candidate is comfortable working outdoors in variable field conditions and able to manage physically active tasks like installing chambers, collecting plant and soil samples, and taking field measurements. Attention to detail and careful, consistent data recording are essential. Familiarity with crops and weeds is an asset, as is basic experience with spreadsheets or data entry. Most importantly, we're looking for someone reliable, curious, and motivated to learn — no prior research experience required, just enthusiasm for hands-on agricultural science.

Research area: My research focuses on environmental materials, waste valorization, water and soil remediation, and sustainable resource recovery. Our MATTER research team and Northern Analytical Laboratory Services at UNBC develop and evaluate advanced materials, including biochar, zeolites, metal-organic frameworks, and bio-based adsorbents, for treating contaminated water, wastewater, air, and soil. We also work closely with municipal and industrial partners to translate laboratory findings into practical environmental solutions for northern and rural communities.

Student roles:
The student will assist with the experimental and analytical components of the project under the supervision of Dr. Hossein Kazemian and members of the MATTER research team. Their role will include reviewing relevant literature on biochar production, municipal waste valorization, and soil remediation; helping prepare feedstock and soil samples; supporting laboratory-scale biochar production; and conducting physicochemical characterization of biochar and amended soils.

The intern may participate in experiments evaluating the effect of different biochar types or application rates on soil quality, nutrient retention, contaminant mobility, and remediation potential. Depending on project progress and sample availability, the student may also assist with analysis of municipal wastewater residuals, contaminated soils, or biochar-treated soil systems using available facilities at UNBC and Northern Analytical Laboratory Services.

The student will be expected to maintain accurate laboratory records, follow safety procedures, analyze and summarize experimental data, and contribute to figures, tables, and short technical summaries. They will participate in regular research group meetings and may help prepare material for partner updates, conference abstracts, or manuscript development. Through this role, the student will gain practical experience in applied environmental research, circular economy approaches, and community-partnered sustainability solutions for northern British Columbia.

Skills required:
The student should have a background in environmental science, chemistry, chemical/environmental engineering, soil science, materials science, or a related discipline. Prior experience with laboratory work, sample preparation, analytical chemistry, soil or water testing, and basic data analysis is desirable. Familiarity with biochar, adsorption, wastewater treatment, contaminant remediation, or materials characterization would be an asset. The student should be careful, organized, safety-conscious, and able to work both independently and as part of a multidisciplinary research team.

Research area: I work in the areas of swine health and production, epidemiology, biostatistics, diagnostics, and surveillance.

Student roles:
The professor will closely guide the student during the first few weeks. After this initial orientation period, the student will gradually take the lead in conducting the research, while continuing to receive guidance from the professor and other laboratory members. The goal is for the student to develop stronger domain knowledge, gain practical experience in synthesizing scientific information, and ultimately contribute to the preparation and submission of a manuscript for peer-reviewed publication.

Skills required:
The ideal candidate will demonstrate a strong command of written and spoken English and have prior experience in animal health and production. A good working knowledge of French will be considered an advantage. Resilience and a demonstrated ability to work with others are also important.

Research area: My research fields concern industrial ecology and climate change mitigation. My work is also focused on determining the effects of climate change on the biogeochemistry and soil-atmosphere gas exchanges in agriculture and from boreal and subarctic ecosystems.

Student roles:
The candidate will take part in all the works related to Carbone boréal: measurement of stand productivity indices, soil sampling and related treatments, sample processing in laboratory, data compilation, learning the basics of biological carbon sequestration and on the science of climate change. The successful candidate will be based at Carbone boréal and the Chair in eco-advising at the Université du Québec à Chicoutimi. This traineeship also gives the opportunity to realize a research project within the framework of a course given in the study program of the candidate.

Skills required:
-Following a study program in biology, environment, chemistry, physic or any other
relevant programs related to the field
-Clear interest for biological carbon sequestration and issues related to climate change
-Capacity to integrate a multidisciplinary research group at the Chair on eco-advising
-Capacity to do physical and meticulous work on the field and in laboratory

Research area: I specialize in the sustainable intensification of horticultural production systems, with a focus on field-scale solutions. My research program takes a pragmatic, multidisciplinary approach to improving labor and input use efficiency, exploring practices such as amendments, crop rotations, cover cropping, and reduced tillage as entry points for long-term systems planning. I am interested in how adapting and combining these practices in ways that are practical for farmers can reduce production costs and environmental impact without sacrificing yield or quality.

Student roles:
The intern will contribute to a field and laboratory experiment evaluating the effects of targeted seedband amendments and biocontrol agents on stand establishment in direct-seeded carrot and onion. The student will be involved in fieldwork, laboratory analysis, and data management and analysis.

In the field, the intern will participate in the preparation and application of amendment treatments at seeding, including the priming of amendments with biocontrol agents prior to application. The student will conduct regular crop monitoring, including germination rate and uniformity assessment, early plant growth characterization, weed competitiveness evaluation, and pest and disease scouting. Environmental conditions in the seedband will be monitored throughout the establishment period.

In the lab, the intern will process soil and plant samples according to established protocols, including basic microbiological assessments of amendment treatments. In the office, the student will be responsible for data entry, organization, and basic statistical analysis of results collected throughout the season. The intern will be expected to engage in the discussion and interpretation of preliminary results.

The student will work under the supervision of the principal investigator and will be guided through key experimental steps, including treatment preparation, application protocols, and data analysis. Autonomy is expected for routine monitoring and data collection tasks. The intern will interact regularly with the research team and farm collaborators.

Skills required:
The project requires a student enrolled in horticulture, crop science, agronomy, agroecology, soil science, or biology, with a genuine interest in sustainable production systems. Prior exposure to vegetable or horticultural production and familiarity with organic amendments and biological inputs are strong assets. Key competencies include field data collection, field experiment setup, and germination and plant growth characterization. Training in soil microbiology and basic statistical data analysis is an asset. Autonomy, rigor, organization, and adaptability to field and lab conditions are essential personal qualities.

Research area: Mixed-species plantations can serve as a tool to enhance forest stand resilience in the face of climate change. Because they have so far been little used by forest managers, we have limited knowledge of interspecific interactions in this context, and even less understanding of what happens below ground.

Student roles:
The student will be required to conduct fieldwork (root sampling or excavation), process these samples in the laboratory, enter and analyze data, and prepare a report or a scientific publication.

Skills required:
The student should enjoy teamwork, as well as fieldwork and laboratory work. Basic knowledge of forest ecology and dendrometry would be preferable.

Research area: My research integrates veterinary epidemiology and social science approaches to better understand the factors influencing animal care practices. Specifically, I examine the barriers that prevent animal owners and veterinarians from implementing best practices in animal care. To address these questions, I draw on both quantitative and qualitative data, including interviews, focus groups, questionnaires, and participant observation.

Student roles:
The student will join a dynamic and supportive research environment, working closely with a graduate student and the supervisor (me) through regular meetings and collaborative discussions.
In this role, the student will explore real-world perspectives by analyzing interview transcripts from veterinarians using thematic analysis, a widely used qualitative research method. This involves identifying patterns in the data by coding meaningful segments of text (e.g., sentences) and organizing them into themes that convey the veterinarians' experiences and opinions. The student may also contribute to developing a thematic map, helping to visually represent key insights from the data.
As the project progresses, there may be opportunities to get involved in writing a scientific manuscript for submission to a peer-reviewed journal. Students who make substantial contributions to the analysis and/or writing will be included as co-authors on the publication.
Overall, this is a great opportunity to gain hands-on research experience, develop qualitative analysis skills, and contribute to publishable work. The student’s contributions will play a meaningful part in shaping research that has a practical impact in the field.

Skills required:
The student does not need specific skills - no veterinary medical knowledge or agricultural knowledge is necessary.

Research area: Our lab focuses on food chemistry and environmental research, from understanding the molecular interactions responsible for macroscopic properties of food (chewiness, crispiness, gumminess, etc), to soil analysis and environmental remediation and mitigation.

Student roles:
The student will analyze municipal and publicly available spatial datasets provided by the City of Orillia to develop a GIS-based heat vulnerability and cooling infrastructure assessment. They will be responsible for preparing a clean and well-documented geospatial database and constructing a heat vulnerability index that integrates environmental exposure and socio-demographic sensitivity indicators.
The student will generate spatial analyses and maps identifying heat-vulnerable areas and evaluate the distribution and accessibility of existing cooling infrastructure relative to those areas. Using these results, they will develop a ranked set of priority locations for cooling interventions such as tree canopy expansion, shade infrastructure, and public cooling amenities.
A final report and set of GIS outputs will present the methodology, findings, limitations, and recommendations to support municipal climate adaptation and infrastructure planning. Work will be conducted at Lakehead University with regular check-ins with City staff and the faculty supervisor for guidance and project oversight.

Skills required:
This project is open to undergraduate students (with at least three years completed) in Science, Engineering, Economics, Urban Planning, or Social Sciences. Intermediate to advanced ArcGIS skills are essential - basic familiarity is not sufficient. Applicants must be proficient in spatial data analysis, mapping, performing overlays, and working with geodatabases. Python coding (intermdiate to advanced) is a very much appreciated asset (but not a requirement). The ideal student is highly self-motivated, detail-oriented, and comfortable working independently on a self-directed project. Strong English writing skills are required to produce a clear, professional-quality report.

Research area: My research focuses on forest ecosystems and bridges topics including geography, ecology, and urban planning, using a diverse range of approaches. The primary research method I use is dendrochronology, which investigates environmental change through annual rings of woody plants. I am a proponent of incorporating fieldwork into internships and I am building a research program on urban forests and how these urban ecosystems are managed and change over time. The fieldwork can include both physical sciences (e.g. forest inventory work, tree coring, soil collection, etc.) but also social sciences (e.g. potential interviews, surveys, developing education, outreach, and interpretation resources).

Student roles:
The student working on this research project will play an active role in field, lab, and office-based research settings. The student will spend about one-half or more of their time collecting data at research sites. The primary research sites will be located in the city of Camrose (student must have appropriate personal gear -- such as closed toe sturdy footwear, rain jacket, etc.). Data collection could include vegetation measurements and collection of samples from trees and shrubs or potentially involves conducting surveys, interviews or public engagement. During his/her time in the field, the student will gain experience in navigating to sites, experimental design and plot lay-out, vegetation sampling techniques and will learn some plant identification and taxonomy skills. In the lab setting, the student will gain experience in processing of tree samples, including organizing vegetation samples and also spatial analysis. Time will also be spent by the student gathering and reviewing relevant literature related to the research program, entering data collected in the field, and potentially analysis of the results. In addition the student will be exposed to other interdisciplinary field and literature research that is ongoing in at the Augustana Campus of University of Alberta. The student will also have the opportunity to present their work at the research assistant symposium on campus in August as well as other potential venues for members of the public. Each student brings their own skills, experience and interests and I always try and tailor projects to areas of strength while encouraging students to develop new skills and approaches.

Skills required:
1. Background and/or interest in botany, biology, ecology, geography, remote, sensing, geomatics, urban planning or related field is required.
2. Attention to detail, hard-working, good communication skills and a team player
3. Willingness to conduct field work in all weather conditions
4. Willing to be involved in diverse project activities and learn new skills
5. Previous fieldwork experience (including data collection, GPS & navigation, etc.) is a strong asset

Research area: This research lab specializes in perennial fruit crops and their wild relatives. We work at the intersection of plant agriculture and data analytics to quantify and characterize trait and genomic variation. Ongoing work includes projects on grapes, strawberries, blueberries, and apples, some of the most important fruit crops in both Canada and globally. We use computational tools and diverse data sets including herbarium records and genomics to improve our understanding of fundamental plant biology and provide evidence-based recommendations for plant breeding, management, and conservation strategies.

Student roles:
The student will contribute to a phenological modeling project using digital herbarium records to examine how reproductive development in berry species has responded to climate change.
The project will begin with a literature review to summarize past phenology work in perennial fruit species including berries. At the same time, the student will begin to score phenological stages (e.g., flowering, fruit development, ripening) digitized herbarium specimen images. The student may potential score photographs, such as those shared on iNaturalist.
The student will use R programming to integrate historical climate records based on geographic coordinates for each specimen scored. Next, statistically modelling will be used to examine the effect of temperature and year of collection. This may involve a combination of linear and mixed models.
The student will document all workflows using reproducible research practices and write a final report summarizing their results and conclusions. All data and code will be organized for future lab use.

Skills required:
The student should have a background in botany, plant biology, plant ecology, or a related field. The ideal student has some experience with the R programming language and be comfortable working with large datasets. The student is also expected to be familiar with scientific literature review to place the results of their work into a broader context. The student should be a strong writer and work well both independently and as a part of a team. There is some repetitive work during data collection and the student should be organized, detail-oriented, and have strong time management skills.

Research area: Dr. Haorui Wu is the Canada Research Chair in Resilience With an interdisciplinary background, his community-based interdisciplinary research and emerging practice have nuancedly explored disaster-driven redevelopment of human and non-human settlements through the lens of environmental justice and social justice in the global context of climate change, disaster, and willful acts of violence. His innovative socio-ecological protection strategies aim to stimulate the transdisciplinary application of engineering, social, cultural, ecological, economic, and political dimensions into the empowerment of grassroots-led community development initiatives that enhance inhabitants and co-inhabitants’ health and well-being.

Student roles:
The students will be engaged into the following three tasks:
• Task 1- Preparedness Awareness (survey): to quantitatively assess CAGs’ awareness, desires, and strategies regarding pre-disaster preparedness for their companion animals;
• Task 2 - Preparedness Experience, Needs, and Challenges (interview): to qualitatively explore CAGs’ experience, needs, and challenges in assisting their animals pre-, peri-, and post-disaster; and
• Task 3 - Preparedness Recommendations: to host a service provider-client workshop to develop actionable recommendations on building an animal-inclusive disaster plan.

Skills required:
Students should be familiar with the literature of (1) hazards and disaster research and (2) human-animal interactions, qualitative and quantitative data analysis.