Future graduate student research opportunities

We are looking for students interested in designing next-generation Solid Li/Na-ion batteries to transform the future of energy storage. We welcome students interested in materials science and engineering, as well as AI-assisted design of chemical structures.

We seek to uncover the principles, cues, and heuristics that guide how people think about the social world. Our research explores how children and adults understand other people's thoughts and emotions, as well as concepts like ownership, rights and responsibilities, and belonging. We also examine how people distinguish fantasy from reality and determine whether events are possible or impossible. Our recent work suggests that children and adults often make these judgments by inferring past events (historical reasoning) and by thinking about how things could have turned out differently (counterfactual reasoning).

Interested in creating sustainable energy systems for vibrant economies and happy communities? Professor Jessie Ma is looking for MASc/MES students and /or PhD students at the University of Waterloo to join us in exploring deep electrification through distributed energy resources.

In recent years, the rapid pace of climate change has been evident by increasing intensity and frequency of weather extremes, which has an adverse impact on the safety and operability of infrastructure systems. Current design codes assume that climate-induced loads are stationary, i.e., their occurrence pattern and intensity do not change over time. However, this assumption is not likely to be valid in the future, since the climate change is causing temporal variations in the frequency and intensity of weather extremes, such as rain, snow and ice storms, and high wind events. 

Our work explores how photons interact with living matter, and how those interactions can be transformed into meaningful biomedical insight. We are interested in seeing what is usually hidden: subtle changes in tissue, chemistry, and structure that precede disease, progression, or healing. The goal is not just sharper images, but deeper understanding.

Our research team is developing a Generation Risk Assessment (GRA) models to optimize the performance of power generation systems. As a part of this project, we are developing a system reliability coupled with the Bayesian updating framework in order to facilitate dynamic updating of risk and reliability estimates as new inspection and surveillance data become available. There is an opening for a fully-funded PhD student in this research project. 

Global efforts to combat climate change are driving a fundamental transformation of electric power systems. Increasing integration of renewable energy resources (RESs), rapid growth in direct-current (DC) loads driven by electric vehicle (EV) charging, and the modernization of aging infrastructure through high-voltage DC (HVDC) links are accelerating the transition from traditional alternating-current (AC) grids toward hybrid AC–DC power systems.

Power electronic inverters serve as the critical interface between AC and DC systems. However, most deployed inverters today are grid-following (GFL), meaning they inject current based on measured grid voltage and frequency. While GFL inverters perform well in strong grids, high penetration levels can lead to instability in low-inertia or weak power systems. Grid-forming inverters (GFMIs), which locally regulate voltage and frequency, offer improved stability and enable islanding and resilient operation. Despite their advantages, widespread integration of GFMIs presents significant technical challenges in control, protection, and interoperability. This project aims to address these challenges by developing advanced control and protection solutions for inverter-dominated power grids. 

The widespread deployment of electric vehicle (EV) charging stations in residential areas faces several critical challenges: (i) limited availability of parking spaces, (ii) insufficient power distribution capacity to meet growing charging demands in densely populated neighbourhoods, and (iii) the high cost, long deployment timelines, and limited scalability and resiliency of state-of-the-art charging infrastructure, particularly during power system outages.

This project aims to address these challenges by developing a compact, low-footprint EV charging station based on a microgrid architecture. The proposed system will be capable of reliable operation during grid outages while minimizing adverse impacts on the utility grid under normal operating conditions. By integrating local energy resources and intelligent control, the charging station will offer enhanced resiliency, ability to expand, and cost-effectiveness compared to conventional solutions.

3D maps are essential for autonomous navigation by vehicles and drones as well as augmented reality applications. We have developed a robust real-time 3D SLAM mapping system that is also to render (using 3D Gaussian splatting) an environment to produce a realistic 3D environment that can be used in simulation in a physics engine for training vehicle control using Reinforcement learning. SLAM and other 3D reconstruction methods assume a relatively static environment while most environments are not. We are exploring methods to deal with dynamic environments for building these maps. Other applications of interest include biomedical applications such as cacheters with camera-on-tip endoscopes, space, mining and any drone application.

Join a team researching micro, nano, and quantum resonators created by the interaction of light, (lower frequency) electromagnetic fields, and micro/nano-scale mechanical structures with a view to; discover new phenomena, learn how to integrate then to best advantage and create novel sensors.