Current research degree projects

Explore our current postgraduate research degree and PhD opportunities.
Explore our current postgraduate research degree and PhD opportunities.
The analysis of financial risk relies on chaotic systems, which are difficult to predict due to their sensitivity and complexity. In this project, you will build on existing machine learning research to create effective prediction approaches, and work alongside experts in chaos modelling, machine learning, and finance to achieve this.
This project aims to develop cutting-edge 3D X-ray imaging methods to improve histopathology and tissue diagnostics. It will advance non-destructive µCT -based imaging of histological specimens to guide sampling, reduce diagnostic error, and support spatial -omics. Based at the interface of engineering and medicine, this project combines imaging science, pathology, and translational biomedical research.
This PhD project builds on a newly funded NIHR research aiming at predicting response to methylphenidate (the most common medication for ADHD), based on pre-treatment clinical, cognitive, and physiological characteristics. Ultimately, this will help tailor treatment options and thus improve patients’ outcomes.
This project pioneers deep learning for turbulence modeling, focusing on wall-bounded flows. By combining convolutional neural networks (CNNs), generative adversarial networks (GANs), and physics-informed methods, it aims to develop hybrid predictive models that overcome current limitations. The research supports scalable, accurate simulations of multi-scale phenomena, advancing computational design across energy, transport, and biomedical applications.
Modern lightweight space structures face harsh environments and often exhibit nonlinear dynamics due to contacts, friction, and geometric nonlinearities. This project combines numerical, analytical, and experimental methods to develop physics-informed machine learning tools for efficient nonlinear system identification, enabling accurate modelling and validation of the next-generation space technologies.
Alzheimer’s disease (AD) is a neurodegenerative disease, with a complex biology. In this PhD project, we aim to explore the anti-inflammatory and neuroprotective properties of hydrogen sulfide (H2S) compounds derived from Brassica species, for example: broccoli. The results may lead to a novel treatment options for AD.
This project focusses on the development of a next-generation high-fidelity topology optimisation (TopOpt) framework for thermofluid systems. It aims to advance simulation-driven design tools to automatically generate complex flow and heat transfer structures with superior performance to conventional designs.
Transport symbolism, which refers to what a user perceives their travel mode says about them, is an important influencer of modal choice. This project builds upon qualitative work, by quantitatively examining how different transport users across varying cultures rate symbolic considerations relative to instrumental measures when choosing a travel mode.