School of Science & Technology Doctoral Studentships

Intelligent Wound Dressing for Accelerated and Controlled Healing of Burn Injuries (i-Heal)

This studentship aims to address the important clinical issue that burn wounds can be the most traumatic and physically debilitating injuries, leading to chronic irreversible scarring and increased mortality.

Innovative Sensor-Cloud for Sewer Monitoring and Management

This studentship aims to address one of the key technical challenges surrounding the ‘storm overflow of raw sewers into UK waters’ faced by the UK water companies.

Modelling the economic and environmental cost of raw materials for the global energy transition to 2050 using integrated life-cycle assessment of renewable energy systems and energy network modelling

This studentship will offer the student a chance to work with experts in the areas of system engineering, life-cycle analysis, environmental modelling, big data processing, renewable energy, and electrical networks.

Network Intelligence for Multipath Support in 5G/6G Networks

You will have the opportunity to work on efficient application of artificial intelligence and machine learning in support of traffic steering, switching and splitting for multipath delivery of Internet services in 5G/6G networks.

Countering Adversarial Attacks in Deep Reinforcement Learning Agents

This project aims to develop novel solutions to the potential attacks on DRL agent and will focus on the less explored impact on DRL agents.

Artificial neural network based prediction formula for the behaviour of perforated steel shear walls reinforced with fibre-reinforced polymer sheets

This studentship will afford the student a chance to go deep in the areas of seismic design of steel structures, optimisation, advanced modelling, and machine learning.

Hydrodynamics of Novel Scour Protection Countermeasures for Monopile Offshore Wind Turbine Foundation

This studentship will afford the student a chance to go deep in the areas of fluid-soil-structure interaction, scouring and costal protection.

The influence of pile stiffness on pile capacity at working load

The research project investigates alternative low stiffness pile loads. The potential benefits could be the reduction in carbon heavy materials and the ability to repurpose existing sites during redevelopment.

Two-phase Cooling Systems for Electric Vehicle Battery Packs

This studentship afford the student a chance to go deep in the areas of multiphase flows and heat-transfer enhancement, as well as in technologies relevant to vehicle electrification.

SAFER: StAll FluttER on conventional and innovative wing structures

This studentship will afford the successful student a chance to gain deep understanding of unsteady aerodynamics and aeroelastic instabilities, as they work under with the academic supervisors and the industrial partner.

Failure modelling and lifetime prediction of structural battery fiber composites for next-generation electric aircraft

This studentship aims to develop a computational framework capable of simulating damage and failure in such multifunctional energy-storing composite materials/structures.

Multi-domain modelling of microsatellite propulsion systems with green propellants

This studentship will afford the student a chance to go deep in the areas of space propulsion, spacecraft design, energy storage and power management with an option of optimisation or control.

Numerical and experimental investigation of turbo-expanders for organic Rankine Cycles

This studentship allows the student a chance to explore fundamental features of high-speed two-phase flows of non-ideal fluids. These skills are expected to be sought after highly by industry.

Symmetry Resolved Entanglement in Quantum Field Theory

This studentship will involve work on Entanglement Measures, with a focus on a recently proposed measured known as Symmetry Resolved Entanglement Entropy.

Phonon Effects on Transport in Weyl and Dirac Semimetals

This studentship will allow the student to acquire a strong expertise on topics at the forefront of research in an exciting and rapidly expanding field of theoretical condensed matter physics.

Higher-derivative systems with benign ghosts from integrable systems

This studentship involves working on integrable classical and quantum field theories.

Mathematical theory of tumour evolutionary modes

This studentship aims to develop a general mathematical understanding of how the diverse evolutionary modes observed in human tumours arise from their evolutionary parameter values and spatial structures.

Geometric and topological methods for segmentation and analysis of three-dimensional cells observed with Electron Microscopy

This studentship allows the student to dive deeper into the areas of medical image processing and computational topology and geometry.

Next Generation Blockchains with Sharding

This studentship lies at the interface of distributed systems and cyber security, with the aim to develop the next generation of blockchains through a method known as sharding.

Improving Predictive Models with Causal Structure Learning

This studentship offers a chance to go deep in the areas comprising machine learning, statistical learning, and the acquisition of managerial skills to develop, manage, and communicate the research findings.

Data Visualisation mediated through natural language interaction

This studentship is at the forefront of research in Machine Learning, Visual Analytics and Information Visualisation.

Conservative Bayesian Assessment of Classifiers used for Malicious Network Activity Detection

The studentship will involve gaining an understanding of ML-based cyber-defence tools and developing novel Bayesian inference algorithms for the assessment of ML-based cyber-defence tools.

Weakly Relational Domains and Abstract Interpretation for Cyber

The studentship will investigate integrating work on lightweight and powerful Weakly Relational Domains into a non-relational abstract interpretation framework, and use the resulting system to analyse benchmark programs.

An Investigation of Student Learning in Computing Education Research

This studentship allows the student to dive deeper into the areas of student learning in computer science.