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Digital twining of energy hubs to accelerate transport and power grid decarbonization

This PhD research opportunity focuses on developing digital twin technology for energy hubs that interface with both transport networks and power grids to support decarbonization. Transportation is currently the largest contributor to greenhouse gas emissions in the UK, and 61 percent of the UK's railway tracks are non-electrified. Energy hubs are novel microgrid technology that transform power supply for electrifying transport networks from inflexible loads to flexible demand. The project aims to address key challenges in digital twinning technology including data sharing, interoperability among digital platforms, and connectivity. The research will develop cross-sector data sharing architecture, create standardized data models for semantic interoperability, establish policies for secure data sharing, and enable fast and effective management of multiple energy hubs to improve flexibility of traction power networks and support cost-effective transition to a low carbon society.

Digital Twins for Liver Cancer Using Medically Informed Machine Learning

This PhD research project develops digital twin models for the human liver for use in liver cancer treatment planning and optimisation. The project creates an image-based computational model of the liver with realistic anatomical variability, structure, and aspects of functionality. Digital twins have many use cases including training clinicians, testing computational algorithms, planning patient-specific treatments, and enabling virtual in-silico trials for evaluating novel treatments. The project focuses on hepatocellular carcinoma (HCC), the most common type of primary liver cancer in adults and currently the most common cause of death in people with cirrhosis. The research will develop a liver function map as part of a digital twin model using biomarkers from pre-operative MRI to estimate tissue-level inflammation, fibrosis, fat content, cirrhosis, and hepatocyte uptake extraction rate. This combines magnetic resonance imaging with medically informed machine learning (MIML) techniques. The project seeks to exploit multi-modal imaging (CT, MRI) and novel data-driven machine learning methods to develop and validate the digital twin model. The liver function maps will be paired with anatomical shape models of the liver and computational algorithms for generating the vasculature of the liver and the tumour, creating the first ever digital twin model of the human liver. MIML techniques will be used to reduce model overfitting and ensure consistency with existing medical knowledge about tumour pathophysiology, vascular function, and tissue response to radiotherapy.

EPSRC Doctoral Landscape Award 2026/27: Electronic & Electrical Engineering

The EPSRC Doctoral Landscape Award offers 2 talented budding researchers the opportunity to join the thriving community of leading researchers within the Faculty of Engineering and Physical Sciences at the University of Leeds. These are highly competitive studentships that provide full tuition fees, together with a tax-free maintenance grant (currently £20,780 for academic session 2025/26) for 3.5 years, along with comprehensive training and support. This opportunity is open to UK applicants only and selection is based on academic merit through a competitive process. Applicants study in a world-leading research environment (REF 2021) with access to UK-leading facilities, close industry links, professional skills development, and comprehensive wellbeing support. The award is linked to specific research projects in the School of Electronic and Electrical Engineering, including topics such as carrier transport modelling in semiconductor lasers, digital twinning of energy hubs, digital twin of open radio access network, and terahertz frequency devices for future communication systems.