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As climate change increases the frequency and severity of disasters, proactive planning for post-disaster housing recovery is essential to mitigate long-term social and economic disruption. Computational models can support this planning by simulating potential recovery trajectories, yet many existing approaches are limited by overwhelming data requirements or narrow applicability to past events. Our work focuses on developing novel computational tools to improve how we manage disaster risk. These can include computational simulations using agent-based models or computer vision-based algorithms to study post-disaster recovery in communities.
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.
We are looking for ambitious graduate students to conduct projects within the Wise Judgment Consortium, which brings together an international team of researchers to study how culture shapes the way we make decisions. Our work is highly interdisciplinary, combining psychology, natural language processing (including Large Language Models), computational modelling, and psychometrics to understand the complex ways cultural, ecological, and situational factors influence everyday decision-making.
This project will utilize high-speed imaging, lasers and instruments to evaluate explosion risk in BESS facilities. A reduced-scale enclosure with optical accessibility will be developed, with explosions simulated by recreating the gas mixtures found from thermal runaway vent gas measurements. Flame acceleration will be induced to generate turbulence by incorporating obstacles into the enclosure that are representative of battery racks in BESS enclosures. The results of this work will help inform future BESS enclosure design and gas venting strategies.
Work on an exciting project focused on developing a high-throughput genomic library of C. difficile to investigate stress defense responses and the molecular mechanisms of antimicrobial resistance. The successful applicant will employ cutting-edge approaches in molecular microbiology, genomics, bioinformatics, and high-throughput phenotypic screening.
Computer Vision for Smart Structure Laboratory (CVISS) at the University of Waterloo, led by Dr. Chul Min Yeum, invites applications for graduate studies (Direct Ph.D. and Ph.D.) in Civil and Environmental Engineering. Our lab focuses on practical, application-driven research, utilizing advanced technologies to integrate intelligence into the physical built environment, aiming to bolster infrastructure safety and resilience.
This position focuses on developing mobile robotic sensing and analysis systems, including unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), for infrastructure inspection and management. Additionally, the role involves designing intuitive human-robot interaction frameworks for collaborative inspection and teleoperation. Students who have experience in mobile robotics, computer vision, and augmented/virtual reality are recommended to apply for this position.