This research develops electrostatic artificial muscles for underwater robots inspired by the movement of fish and sharks. Unlike noisy propeller-driven systems, these soft actuators enable quieter, more efficient swimming that minimises disturbance to marine ecosystems, offering a promising alternative for environmental monitoring, reef surveys, and underwater infrastructure inspection.

This research uses artificial intelligence to accelerate scientific simulations by learning patterns from traditional mathematical models. Rather than replacing physics, the AI predicts efficient starting points for complex calculations, producing accurate results much faster. The approach could dramatically speed up research in fields such as medicine, engineering, and climate science.

This research investigates how the choice of time interval affects reinforcement learning models for sepsis treatment in intensive care. By comparing one-, two-, four-, and eight-hour patient timelines, it demonstrates that shorter intervals better capture patient dynamics, improve estimated survival outcomes, and may enable more effective AI-assisted clinical decision-making.

This dissertation presents temporal-difference learning methods for building world models that enable intelligent agents and robots to predict, plan and act. It introduces TD-MPC, which learns compact latent dynamics, rewards and value estimates for model-predictive control, and TD-MPC2, a more robust successor requiring little task-specific tuning. Experiments demonstrate faster real-world robot learning, scalable multitask performance and a unified model spanning 200 tasks across ten domains. The research also examines interactive generative world models and their tendency to hallucinate physically implausible futures. Targeted data collection in uncertain regions substantially reduces these errors, suggesting routes toward safer, uncertainty-aware autonomous systems and robotics.

This research uses AI-powered markerless motion capture to preserve Indigenous cultural dances as digital archives. By recording thousands of movement data points, it safeguards intangible cultural heritage for future generations. The work aims to extend this technology globally, ensuring every culture has the tools to preserve its unique traditions.

This research develops a fast algorithm to identify rare gravitational lenses in images from the Euclid Space Telescope. By analysing how galaxies bend light, it maps the distribution of invisible dark matter, helping astronomers investigate one of the greatest mysteries in physics: the nature and composition of dark matter.

This research develops a brain-inspired optical imaging system that mimics human vision to reconstruct objects hidden by fog, smoke, and biological tissue. Combining event-based cameras, spiking neural networks, and neuromorphic processors, it enables fast, energy-efficient imaging with applications in autonomous vehicles, emergency response, and non-invasive medical diagnostics.

This research develops Roblonski, a compact robotic platform that automates photoredox chemistry using microscopic droplets and visible light. By reducing chemical use, waste, and manual effort by over 90%, it generates high-quality data for AI-driven discovery, paving the way for faster, greener, and more intelligent self-driving chemistry laboratories.

This research investigates how the brain makes decisions under uncertainty by studying mice navigating reward-based mazes. Rather than relying on memorisation, mice continually update mental models through active exploration. These findings improve our understanding of anxiety disorders and may inspire more adaptive artificial intelligence systems.

This research teaches AI to understand and generate the sense of touch by combining visual information with high-resolution tactile data. The technology enables realistic digital textures, improves online shopping, enhances virtual experiences, and creates accessible tactile graphics for blind and low-vision users, making AI more inclusive and human-centred.