Students
I work with both graduate and undergraduate students, tailoring opportunities to each stage of academic development. Undergraduate students are offered short term projects designed to introduce them to the skills and thinking required in robotics research, providing a meaningful taste of the field without an overwhelming commitment. Graduate students engage in longer term research aligned with their degree objectives, with potential funding opportunities available. Across all levels, I focus on developing two types of skills that I believe are essential for remaining competitive in today's employment landscape.
01
Hands on learning
Hands on experience is central to my approach to graduate education. Students learn not only the theory behind robotic systems but also the practical skills required to build, maintain, and deploy them in real world conditions. This includes working with mechanical assemblies, electronic systems, embedded software, and sensor integration. By constructing vehicles from scratch, students develop a deep understanding of how design decisions affect performance, reliability, and maintainability. They gain experience troubleshooting hardware failures, iterating on prototypes, and preparing systems for field operations. These skills are invaluable for careers in research, defence, and industry, where the ability to move from concept to functioning hardware distinguishes capable engineers from those who work only in simulation.
02
Theoretical modelling
The second essential skill set is theoretical modelling and its computational implementation. Students learn to develop mathematical representations of robotic systems that can be deployed as control algorithms or digital twins. Digital twins provide a virtual replica of the physical robot, enabling simulation based testing, predictive maintenance, and real time performance monitoring without risking hardware. Beyond classical modelling, students also explore artificial intelligence and machine learning approaches that allow robots to learn from data, adapt to new environments, and improve their performance over time. Together, these hands on and theoretical competencies prepare students for a workforce where the ability to bridge physical systems and their computational counterparts is increasingly valued.
