Current research degree projects
Explore our current postgraduate research degree and PhD opportunities.
275 research degree projects
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Engineering | Electronics and Computer Science
Evaluating defect detectability for iteratively reconstructed industrial X-ray computed tomography data
In this project, you will study the influence that different advanced optimization and machine learning algorithms have on the quality of 3D images generated from X-ray computed tomography measurements of industrial components. The focus is on optimising the ability to detect internal object defects that would compromise component safety. -
Engineering
Machine learning methods for high energy X-ray computed tomography scatter correction
In this industry sponsored project, you will develop advanced machine learning tools to detect and remove scatter artefacts from X-ray images acquired with state of the art, high energy X-ray imaging systems, where scatter becomes the dominant source of image artefact. -
Physics and astronomy
Beyond the standard model physics at colliders
The NExT Institute proposes a project on the `HL-LHC upgrade of the CMS hardware trigger and searches for new physics’. The Compact Muon Solenoid (CMS) experiment will upgrade its Level 1 trigger system for High Luminosity Large Hadron Collider (HL-LHC) operation. -
Electronics and Computer Science | Engineering
Dynamic metasurfaces for advanced signal processing
This project aims to design and build software-controlled smart surfaces to shape wireless signals for advanced applications like analogue computation and next-generation communications. It will pioneer active metasurfaces that dynamically steer, process, and amplify electromagnetic waves, blending theoretical modelling, PCB design, and lab testing to create the foundation for future programmable radio environments. -
Electronics and Computer Science | Mathematical sciences
Responsibility reasoning in multiagent systems
The aim of this project is to develop formal models of accountability and liability using logic, game theory, and agent-based simulation. You will explore responsibility under uncertainty, delegation, and trust, with applications in autonomous systems, digital governance, and ethical AI. -
Electronics and Computer Science | Engineering
Smart materials for wearable electronics with next-gen manufacturing
This project offers a rare opportunity to work with the UK’s only roll-to-roll sputtering system, a brand new and bespoke platform designed specifically for next-generation material discovery. You’ll explore innovative thin-film materials for flexible electronics, developing scalable recipes that bridge cutting-edge research with real-world commercial compatibility through high-throughput, fabric-friendly processes. -
Engineering
Development of high-performance miniaturised ultrasonic devices for precision minimally invasive surgery
This project, in the field of ultrasonic surgery, focuses on the development of cutting-edge miniature ultrasonic devices targeted for bone surgery. It seeks to advance the current state-of-art design by introducing new configurations and incorporating novel structures in miniaturised devices to enhance precision and improve clinical outcome. -
Engineering
Hybrid turbulence modelling: bridging physics and data
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. -
Engineering
System identification of nonlinear space structures via physics-informed machine learning
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. -
Engineering
Thermofluid topology optimisation
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.