Current Graduate Opportunities

MSc & PhD positions: Quantum Mechanical Simulations for Catalyst Discovery and Clean Energy - Prof. Leanne Chen

The Computational Electrochemistry Laboratory is looking for curious and motivated students to conduct research at the cutting edge of catalyst discovery for clean energy applications. Our recent publications have focused on the design and optimization of direct ammonia fuel cells. Ongoing projects include large-scale screening of electrocatalysts to accelerate discovery and developing state-of-the-art theoretical models for the electrochemical double layer.

Project keywords: quantum chemistry, density functional theory, electrocatalysis, simulations, energy storage and conversion

Start date: as early as January 2026. Expressions of interest accept year-round.

MSc & PhD positions: Detection of PFAS in Environmental and Biological Systems - Prof. Jeremy Gauthier

The Gauthier Lab is currently looking for PhD and Masters students to develop new analytical chemistry approaches for the measurement of per- and polyfluorinated alkyl substances (PFAS) in a variety of systems including surface waters, biosolids, food packaging, agricultural produce, and animals and humans. Students will gain experience in NMR, mass spectrometry, and environmental and analytical chemistry techniques.

Project keywords: Analytical chemistry, NMR, environmental chemistry, PFAS

Start date: January 1 or September 1, 2026. Applications will be review regularly.

MSc position: Human-centered intelligent transportation - Prof. Yulia Kotseruba

My lab builds computer vision systems inspired by human vision and cognition for driving applications. We study how drivers and pedestrians behave in traffic, what do they look at, and how they make decisions. Our goal is to create agents that behave more proactively and in a more human-like manner than typical bots that mainly react to events.

This position involves working with large volumes of real-world data from cameras, eye-trackers, and car sensors and building machine learning (ML) models that predict what drivers or pedestrians will do next. We also use this data to create better simulated road user agents.

The skills you will gain include: training models on large amounts of data, using advanced data analysis tools to investigate model performance and data, conduct studies with human participants, design interactive simulated environments, gain valuable domain knowledge, and learn more about human cognition in the real world.

Qualifications:

• Experience/courses in computer vision
• Familiarity with HCI and/or cognitive science
• Experience with Python, Machine Learning frameworks (PyTorch, Tensorflow)

Keywords: computer vision, cognitive systems, AI, transportation

How to apply: Complete application form

Start date: Fall 2026 or earlier. Applications reviewed on rolling-basis.

MSc position: Machine learning & AI in emotional decision-making - Prof. Andrew Hamilton-Wright

Study the use of Machine-Learning (“AI”) tools in the context of human-in-the-loop decision making for treatment of emotional regulation in children. Dr. Andrew Hamilton-Wright, (Computer Science), and Dr. Kristel Thommasin (Psychology) in collaboration with researchers at the Centre for Addition and Mental Health, Toronto (CAM-H) are studying biobehavioural regulation of negative emotion in children.

The focus of this project is the use of responsible, reliable and ethically applied machine-learning for transparent and trustworthy decision making.

You will learn to apply association mining based machine-learning techniques to create open, explainable and trustworthy diagnostic tools that will be used by human decision makers in real treatment scenarios to improve health outcomes.

This isn’t AI slop based on LLM models – this is intentional and rigorous application of solid science to create tools based on an informed understanding.

Learn how to apply machine-learning tools effectively and for good.

How to apply:
Email Prof. Andrew Hamilton-Wright at andrew.hamilton-wright@uoguelph.ca

Start date: Fall 2026 but can start earlier.

MSc and PhD positions: Cybersecurity, AI and Machine Learning - Prof. Wenjing Zhang

The AI Security Lab invites applications for multiple funded masters and doctoral positions in cybersecurity and artificial intelligence and machine learning. Our lab advances secure and privacy-preserving machine learning, trustworthy generative AI models, and practical security defenses for real systems. The lab is supported by funding from the Natural Sciences and Engineering Research Council of Canada (NSERC), Mitacs, Canadian industry partners, and the University of Guelph start-up fund. We are currently collaborating with physicians from St. Joseph’s Healthcare Hamilton and researchers from the University of Florida, the University of Arizona, and Queen’s University.

Qualifications

• Background in computer science or computer engineering is required.
• Preference for applicants with experience in AI and machine learning or cybersecurity.
• Strong programming skills with a solid mathematical background
• Research publications, open-source contributions, or relevant industry or lab experience will be considered strong advantages.

How to apply:
Send your CV and academic transcript to Dr. Wenjing Zhang - wzhang25@uoguelph.ca

Start date: Winter 2026 and Fall 2026. Applications will be reviewed regularly.

PhD position - Statistical modelling of compositional data - Prof. Zeny Feng

The research project is funded by both the NSERC discovery grant and the Collaborative Research Term funding program from Canadian Statistical Sciences Institute. Statistical modelling of compositional data has a wide applications, e.g., microbiome data analysis using metagenomic approach.

Project keywords: Compositional data, high dimensional data, multivariate counts, metagenomics, microbiome, multinomial-based, spatio-temporal data analysis, model assessment

Start date: January 1, May 1 or September 1, 2026

MSc & PhD positions: Quantum Mechanical Simulations for Catalyst Discovery and Clean Energy - Prof. Leanne Chen

The Computational Electrochemistry Laboratory is looking for curious and motivated students to conduct research at the cutting edge of catalyst discovery for clean energy applications. Our recent publications have focused on the design and optimization of direct ammonia fuel cells. Ongoing projects include large-scale screening of electrocatalysts to accelerate discovery and developing state-of-the-art theoretical models for the electrochemical double layer.

Project keywords: quantum chemistry, density functional theory, electrocatalysis, simulations, energy storage and conversion

Start date: as early as January 2026. Expressions of interest accept year-round.

MSc & PhD positions: Quantum Mechanical Simulations for Catalyst Discovery and Clean Energy - Prof. Leanne Chen

The Computational Electrochemistry Laboratory is looking for curious and motivated students to conduct research at the cutting edge of catalyst discovery for clean energy applications. Our recent publications have focused on the design and optimization of direct ammonia fuel cells. Ongoing projects include large-scale screening of electrocatalysts to accelerate discovery and developing state-of-the-art theoretical models for the electrochemical double layer.

Project keywords: quantum chemistry, density functional theory, electrocatalysis, simulations, energy storage and conversion

Start date: as early as January 2026. Expressions of interest accept year-round.