Current research degree projects
Explore our current postgraduate research degree and PhD opportunities.
266 research degree projects
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Engineering
Physics informed neural networks as an environmentally friendly alternative to CFD
Physics-Informed Neural Networks (PINNs) provide an innovative, efficient alternative to traditional methods employed in fluid dynamics simulations, delivering Computational Fluid Dynamics (CFD)-level of accuracy at significantly lower computational costs, thus reducing energy use and benefiting the environment. -
Electronics and Computer Science | Engineering
Brain-inspired security primitive for secure hardware in space
Memristors mimic how the brain learns—offering low-cost, secure, and energy-efficient computing. This PhD tackles challenges limiting their adoption, from ageing to variability, to create unforgeable, self-identifying chips. -
Photonics and optoelectronics | Engineering | Physics and astronomy | Geography and environmental science
Material platforms for low-loss photonic integration in the UV-VIS spectrum
Explore advanced material platforms for UV-VIS photonic integrated circuits. This project will develop low-loss waveguides using wide band-gap materials, enabling breakthroughs in biomedical sensing, environmental monitoring, and compact light engines for high-resolution displays. -
Photonics and optoelectronics | Engineering
Modulation-enabled silicon photonics for RGB light engines
Join a cutting-edge project developing silicon photonic integrated circuits for Red, Green, and Blue (RGB) light modulation. You will explore novel approaches to control visible light on-chip, contributing to future technologies in displays, sensing, and optical communications. -
Engineering
Uncertainty quantification to improve the design of civil engineering infrastructure
This project combines ground engineering with statistical uncertainty quantification to support sustainable decision-making for infrastructure construction. Work with real-world data, expert teams, and cutting-edge techniques to reduce over-conservatism in design and construction. -
Engineering
Laboratory testing of weathered clays and mudstones to support transport and energy construction
This project investigates the engineering properties of weathered Jurassic and Triassic mudstones. Using samples obtained during the construction of the HS2 high-speed railway, it explores how these materials respond to construction loading. Results will inform more cost-effective design and construction of transport and energy infrastructure built on mudstone outcrops. -
Engineering | Electronics and Computer Science | Mathematical sciences
Seeing between the pores: data assimilation of mean flows through porous structures
This project combines cutting-edge data assimilation with experimental and computational fluid dynamics to uncover hidden flow properties in porous materials. By tuning RANS model parameters to experimental data, we infer permeability from geometry-based porosity alone—advancing our ability to predict, model, and understand complex flows through porous structures. -
Engineering
Best value public procurement models to deliver battery-electric bus fleet
Many governments who contract their bus services to the market have committed to zero emissions bus fleet. Existing procurement frameworks, however, are not well suited to zero-emission transitions. Research is needed to rectify this to secure best value outcomes and improved air quality within fiscal constraints. -
Engineering
Improving understanding of the inner ear using otoacoustic emissions
This project aims to test our theories of cochlea processes by measuring sounds generated within the cochlea and comparing the results with theoretical predictions. The cochlea generates low-level sounds (otoacoustic emissions) in response to sound stimulation. Novel stimuli will be used to assess the generation mechanisms. -
Electronics and Computer Science | Engineering | Photonics and optoelectronics
Optoelectronic AI processors for cryogenic and space electronics
This PhD project aims to develop error-tolerant AI processors for cryogenic and space electronics using optomemristor technology, which mimics neural networks and offers ultra-fast, low-power computing. Collaboration with Von Ardenne GmbH provides industrial insights into material processing and photonic integration for advanced chip design.