Fixed-term

Overview Superviors: Dr Jing Chen  jing.chen@leicester.ac.uk Dr Catherine John  catherine.john@leicester.ac.uk Dr Tom Ward  tom.ward@leicester.ac.uk Project Description: This project aims to uncover the biological factors that shape lung disease by combining state-of -the-art multi-omics data with advanced computational, statistical and machine learning techniques. Chronic obstructive pulmonary disease (COPD) is a long-term lung condition which causes millions of deaths worldwide each year. The disease process varies significantly from person to person, with some people experiencing severe damage to the lung’s air sacs (emphysema) and others predominantly experiencing inflammation of the airways. Traditionally, researchers have tried to classify COPD into subtypes based on clinical assessments including spirometry (lung function tests) and imaging [1-4]. However, with the exception of eosinophilic disease (i.e. with high levels of inflammatory cells called eosinophils), these approaches showed inconsistent findings that were difficult to reproduce across populations. Treatment strategies tailored to specific subtypes remain constrained by limited mechanistic understanding and a lack of standardised measurements and definitions. A newer way of thinking about lung disease is the “treatable traits” approach, which recognises that chronic airway diseases arise from interactions across complex biological, behavioural, and environmental networks[5, 6]. Instead of placing people into broad disease types, this approach focuses on holistic, multidimensional assessment of pulmonary, extrapulmonary and lifestyle-related traits that are clinically measurable and treatable. Integrating this framework with “omics” and the environment represents a critical opportunity for insight into the biology underlying individual clinical presentations, with potential to transform personalised prevention, diagnosis and treatment.[5] This PhD project will bring together large-scale data from biobanks, including multi-omics, biomarkers and environmental data, to identify and predict COPD clusters defined by treatable traits. You will use advanced computational, statistical and machine-learning approaches to uncover novel biological insights and support the development of personalized risk prediction tools.  This is an exciting opportunity to work at the forefront of respiratory precision medicine and generate results with direct clinical relevance, with potential to improve COPD care for patients. Training and Environment You will join a world-leading Genetic Epidemiology research group. The group currently hosts 17 PhD students and has an exceptional track record in developing talented postgraduate researchers who go on to successful postdoctoral careers in academia and in industry. You will benefit from: expert supervision in statistical genetics, genetic epidemiology and bioinformatics, high-performance computing facilities for large-scale data analysis, links with the Leicester NIHR Biomedical Research Centre, opportunities to collaborate with leading clinicians and functional genomics experts to support translational insight, chances to get involved in public engagement and science communication. Your PhD training will foster well-rounded expertise and skills spanning data science, population health and translational research, positioning you well for further opportunities in this rapidly-growing field.> Expected Outcome By the end of the project you will: generate new insights into COPD heterogeneity, identify biomarkers and trait-based clusters relevant for disease stratification, improve understanding of disease mechanisms underlying individual patient presentations, Contribute to development of tools for personalised risk prediction. Your findings will help lay the groundwork for future personalised medicine approaches, enabling patients and clinicians to make better decisions about COPD care. You will present your emerging findings to academics and clinicians at relevant local, national and international conferences, and will be encouraged to contribute to peer-reviewed publications. Funding The College of Life Sciences Studentship will provide: 3.5 years UK tuition fees 3.5 years stipend at the UKRI rates. For 2026/7 this will be £20,805 per year, paid in monthly instalments International students are welcome to apply but will need to be able to pay the difference between UK and Overseas fees for the duration of study. The fee annual fee difference for 2026/7 academic year will be £19,012.  Costs relating to travel, visa and NHS surcharge will be the responsibility of the student. Entry requirements Applicants must hold: 1st or 2:1 Honours degree (or equivalent),in a relevent subject. University of Leicester English language requirements apply. Informal enquiries Project enquiries should be emailed to Dr Jing Chen jing.chen@leicester.ac.uk Application advice to pgrapply@le.ac.uk How to apply To apply please use the Apply link at the bottom of this page and select September 2026. With your application, please include: CV Personal statement explaining your interest in the project, your experience and why we should consider you Degree certificates and transcripts of study already completed and if possible transcript to date of study currently being undertaken Evidence of English language proficiency if applicable In the reference section please enter the contact details of your two academic referees in the boxes provided or upload letters of reference if already available. Referees cannot be anyone on the project supervisory Team. In the proposal section please provide the name of the supervisors and project title in the space provided (a proposal is not required) In the funding section please specify: PHS Chen Notes Applications will not be considered after the closing date. We will advise you of the outcome by email. Please check the spelling of your referee’s email addresses carefully. Eligibility UK and International applicants are welcome to apply. International applicants please refer to the funding section to ensure you can meet the additional costs.

Supervisors: Dr Maryam Afzali  mad37@le.ac.uk Professor Huiyu Zhou hz143@leicester.ac.uk Profesor Gerry McCann  gpm12@leicester.ac.uk Project description: Diabetes and cardiometabolic disorders are major contributors to heart failure, particularly heart failure with preserved ejection fraction (HFpEF), which remains poorly understood and challenging to diagnose early (Upadhya & Kitzman, 2020; Shah et al., 2016). Patients with type 2 diabetes (T2D) often develop early myocardial remodelling, including changes in fibre alignment, sheetlet orientation, and extracellular matrix composition, which precede overt structural or functional abnormalities detectable by conventional imaging. Early detection of these microstructural changes may be critical for risk stratification, timely intervention, and personalised management of cardiometabolic patients. Diffusion MRI (Basser & Pierpaoli, 2011) enables non-invasive characterisation of myocardial microstructure by capturing fibre orientation, sheetlet architecture, and tissue anisotropy (Sosnovik et al., 2009; Afzali et al., 2024, 2025), providing insights into early pathological processes associated with diabetes, obesity, and metabolic syndrome. This project aims to leverage machine learning to detect and predict subtle myocardial microstructural alterations in individuals with T2D and other patient groups with Stage B and mild symptomatic HFpEF. The student will optimise diffusion MRI acquisition and post-processing pipelines to robustly quantify parameters including mean diffusivity (MD), fractional anisotropy (FA), helix angle (HA), and secondary eigenvector angle (E2A) (Nielles-Vallespin et al., 2017; Gotschy et al., 2021). Machine learning approaches will be applied to extract latent patterns of microstructural organisation and predict trajectories of myocardial remodelling. In addition, emerging large language model (LLM) approaches will be investigated for integrating multimodal datasets, including imaging-derived features, clinical variables, and unstructured health records. These models have shown promise in automated extraction of cardiac imaging parameters and clinical data interpretation (Wahi et al., 2025), and may enable improved interpretability, automated reporting, and translation of complex imaging findings into clinically actionable insights. By integrating diffusion-derived metrics with functional imaging data such as strain, T1/T2 mapping, and conventional cardiac MRI markers, alongside clinical and biochemical parameters, the student will develop interpretable models linking microstructural changes to cardiac performance. These predictive frameworks aim to identify early disease signatures before clinical symptoms or overt imaging abnormalities appear, supporting proactive patient management and personalised therapeutic strategies. The project will employ both cross-sectional and longitudinal study designs to capture disease trajectories, evaluating how microstructural alterations evolve over time in relation to glycaemic control, metabolic health, and cardiovascular function. Multimodal datasets will allow correlation of diffusion metrics with structural and functional imaging, blood biomarkers, and exercise physiology measures, providing a comprehensive understanding of early remodelling in diabetes and metabolic disease. By combining high-resolution imaging, AI-based analysis, and advanced statistical approaches, the student will generate robust, reproducible insights into the myocardial microstructure of cardiometabolic patients. This interdisciplinary PhD provides training across cardiovascular imaging, computational modelling, machine learning, and clinical cardiology. The student will gain hands-on experience in advanced diffusion MRI acquisition and reconstruction, microstructural modelling, AI-driven data analysis, and multiparametric statistical evaluation. Training will include exposure to open-source computational tools, data harmonisation pipelines, and collaboration within Leicester’s cardiovascular imaging group and international research networks. Supervision will be provided by a multidisciplinary team with complementary expertise in imaging physics, AI, and clinical cardiology, ensuring a strong translational focus and alignment with ongoing multicentre initiatives. The expected outcomes of this project include sensitive, reproducible biomarkers of early myocardial microstructural remodelling in diabetes and cardiometabolic disease, alongside machine learning–based predictive models linking structure to function. These outputs will advance understanding of early disease mechanisms, support personalised risk assessment, and provide a foundation for integrating microstructural metrics into clinical trials and preventative strategies. Ultimately, the project aligns with BHF priorities in translational cardiovascular imaging and aims to bridge the gap between advanced imaging research and real-world clinical application, potentially guiding early intervention strategies for cardiometabolic patients. Funding The BHF/College of Life Sciences Studentship will provide: 3.5 years UK tuition fees 3.5 years stipend at the UKRI rates. For 2026/7 this will be £20,805 per year, paid in monthly instalments International students are welcome to apply but will need to be able to pay the difference between UK and Overseas fees for the duration of study. The fee annual fee difference for 2026/7 academic year will be £19,012.  Costs relating to travel, visa and NHS surcharge will be the responsibility of the student. Entry requirements Applicants must hold: 1st or 2:1 Honours degree (or equivalent),in a relevent subject. University of Leicester English language requirements apply. Informal enquiries Project enquiries should be emailed to the PhD supervisor Dr Maryam Afzali   mad37@leicester.ac.uk Application advice email pgrapply@le.ac.uk How to apply To apply please use the Apply link at the bottom of this page and select September 2026. With your application, please include: CV Personal statement explaining your interest in the project, your experience and why we should consider you Degree certificates and transcripts of study already completed and if possible transcript to date of study currently being undertaken Evidence of English language proficiency if applicable In the reference section please enter the contact details of your two academic referees in the boxes provided or upload letters of reference if already available. Referees cannot be anyone on the project supervisory Team. In the proposal section please provide the name of the supervisors and project title in the space provided (a proposal is not required) In the funding section please specify: CVS Afzali BHF Notes Applications will not be considered after the closing date. We will advise you of the outcome by email. Please check the spelling of your referee’s email addresses carefully. Eligibility UK and International applicants are welcome to apply. International applicants please refer to the funding section to ensure you can meet the additional costs.