Current research degree projects
 
  Explore our current postgraduate research degree and PhD opportunities.
 
  Explore our current postgraduate research degree and PhD opportunities.
 
  Navigational drift is a major bottleneck for systems operating in GPS-denied underwater, space, and subterranean environments. This project advances navigation in such conditions by fusing fast, drift-prone classical inertial sensors with stable quantum measurements. You will develop fusion algorithms, explore sensor configurations, and validate performance through simulation and hardware-in-the-loop testing.
This project will explore recent distillation LLM training (e.g. DeekSeek-R1) for the mental health domain, including human-in-the-loop LLM training approaches such as adversarial training and rationale-based learning. These algorithms will be tested on a case study focussing on robust and safe self-help mental health applications for military veterans.
This project will revolutionise electromagnetic defence by creating intelligent surfaces that act as physical neural networks. We will develop metasurfaces that learn in real-time to autonomously counteract jamming, secure communications, and manage sensor signatures, providing a critical advantage in the contested electromagnetic spectrum.
This project will explore how Multiphysics aspects, including heat transfer, fluid flow, structural mechanics and acoustics, can be effectively modelled, understood and integrated into the design of advanced acoustic control technologies that are required across automotive, maritime, aerospace and built environment applications.
Aviation is entering a transformative era defined by emerging propulsion technologies, intelligence, and innovations such as quantum technologies. If you are driven to create high-resolution sensing technologies that enable smarter, data-informed decision-making in aviation, this project offers an opportunity to contribute to the next generation of intelligent aerospace systems.
The project brings ideas from the observation of "in-context learning" in large language models into quantum computing. The aim is to design transformer-inspired quantum circuit architectures that brings in-context choice of families of measurement operators for shadow tomography. This contributes to hybrid NISQ quantum-classical algorithms.
How can we “see” sound in three dimensions? In this project, you will develop intelligent 3-D beamforming methods that fuse advanced acoustic modelling with data-driven learning. 
Harnessing structural disorder to control light offers a new route to highly efficient solar thermal energy harvesting. This project will develop and model hyperuniform disordered metasurfaces, a new class of nanostructured materials that achieve near perfect absorption and minimal thermal losses for next generation solar thermal energy systems.
This project aims to pioneer advancements in the energy-efficient Generative AI models (GenAI), focusing on achieving faster inference times and reduced model sizes without compromising performance and increasing carbon emissions.