About the Project

Asthma is a common condition affecting 10% of the UK population. However, diagnosing asthma is challenging, with 1 in 3 being misdiagnosed and inappropriately treated for the condition, driving avoidable morbidity, mortality and costs.

Asthma causes abnormal narrowing of the airways in the lungs, but the narrowing affects individuals differently (1, 2). Current practice relies on spirometry to quantify the airway narrowing. However, this technique summarises complex airway physiology with an over-simplified measurement approach. Although this reductionist approach facilitates ease of test interpretation in clinical practice, it limits test sensitivity: an abnormal spirometry only picks up 30% of asthma (3). Therefore, there is an urgent unmet need for more sensitive and informative strategies to improve asthma diagnosis.

Emerging techniques such as oscillometry and breath washout tests provide more granular assessments of lung physiology, capturing distinct features such as airway impedance and gas mixing. Together, they offer complementary insights into airway obstruction that spirometry alone cannot provide. Nevertheless, their application as stand-alone tests remains limited and largely pre-clinical. This is due to: (i) rich multiscale data are reduced to simplistic summary metrics, and (ii) the substantial variability between asthma patients. Integrating detailed lung function data through machine learning (ML) methods has the potential to increase the diagnostic sensitivity of physiological measurements.

This PhD will leverage multi-modal, high-resolution, dynamic lung-physiology time-series collected before/after bronchoconstrictor challenge, bronchodilator administration and the initiation of inhaled-corticosteroid therapy (long term treatment for asthma). These data are already available from RADicA study (https://www.radica.org.uk/home.htm) [3], the UK’s largest and most rigorously designed asthma diagnostic study), comprising 500 symptomatic adults and children and 300 age-matched healthy volunteers. We will apply state-of-the-art methods for multi-modal and multi-variate time-series clustering, such as those based on multi-task Gaussian processes (4), to deliver unbiased structure–function phenotyping. In parallel, the student will extend physics-based lung models developed within the supervisory team to create patient-specific “digital twins” that explain the mechanisms underlying observed physiological phenotypes (5).

The student will work in a multidisciplinary supervisory team spanning respiratory medicine, physiology, computational modelling and machine learning, offering outstanding training, the potential for high-impact publications and a strong platform for future funding.

1. Kraft M, Richardson M, Hallmark B, et al; ATLANTIS study group. The role of small airway dysfunction in asthma control and exacerbations: a longitudinal, observational analysis using data from the ATLANTIS study. Lancet Respir Med. 2022;10(7):661-668.

2. Wang R, Bennett M, Willmore L et al. Phenotypes of dynamic changes in impulse oscillometry during methacholine challenge. Eur Resp J, 2024 64(suppl 68): PA3054

3. Simpson AJ, Drake S, Healy L, Wang R, Bennett M, Wardman H, Durrington H, Fowler SJ, Murray CS and Simpson A. Asthma diagnosis: a comparison of established diagnostic guidelines in adults with respiratory symptoms, eClinicalMedicine. 2024;5;76:102813

4. Leroy A, Gupta V, Tint MT, Shu QOD, Yap F, Lek N, Godfree KM, Chong YS, Lee YS, Eriksson JG, Alvarez MA, Michael N and Wang D. Prospective prediction of childhood body mass index trajectories using multi-task Gaussian processes. Int J Obes. 2025;49, 340–347

5. Whitfield CA et al, Model-based Bayesian inference of the ventilation distribution in patients with cystic fibrosis from multiple breath washout, with comparison to ventilation MRI.  Resp. Physiol & Neurobiol. 2022, 302(103919)

Entry Requirements

Applicants should hold (or be about to obtain) a First or Upper Second class (2:1) UK honours degree, or international equivalent, in a relevant subject.

Application Guidance

Candidates must contact the primary supervisor (carl.whitfield@manchester.ac.uk) before applying to discuss their interest in the project and assess their suitability.

Apply directly via this link:

MRC DTP PhD Programme

https://tinyurl.com/m87wpezv

or on the online application portal, select MRC DTP PhD Programme as the programme of study.

You may apply for up to two projects within this scheme. To do so, submit a single online application listing both project titles and the names of both main supervisors in the relevant sections.

Please ensure that your application includes all required supporting documents:

• Curriculum Vitae (CV)

• Supporting Statement

• Academic Certificates and Transcripts

Incomplete or late applications will not be considered. Further details are available on our website: 

MRC Doctoral Training Partnership | Biology, Medicine and Health | University of Manchester

Equality, diversity and inclusion are central to the University’s activities. The full statement can be found here: https://www.bmh.manchester.ac.uk/study/research/getting-started/equality-diversity-inclusion/

Funding Notes

The MRC DTP studentships provide full funding for tuition fees and a stipend at the UKRI rate for four years starting in September 2026. Candidates will need to cover relocation and associated costs (e.g. visa, health surcharges).

Relevant Publications

Dean J, Fowler SJ, Singh D and Beech A. Expiratory flow limitation development index (ELDI): a novel method of assessing respiratory mechanics in COPD. Respiratory Research,2024; 25(1), 357.

Whitfield CA et al. Model-based Bayesian inference of the ventilation distribution in patients with cystic fibrosis from multiple breath washout, with comparison to ventilation MRI, Resp. Physiol. & Neurobiol.2022; 302(103919)

Simpson AJ, Drake S, Healy L, Wang R, Bennett M, Wardman H, Durrington H, Fowler SJ, Murray CS and Simpson A. Asthma diagnosis: a comparison of established diagnostic guidelines in adults with respiratory symptoms, eClinicalMedicine. 2024; 5;76:102813

Leroy A, Teh AL, Dondelinger F, Álvarez MA, Wang D. Longitudinal prediction of DNA methylation to forecast epigenetic outcomes, EBioMedicine, 2025;115:105709.

Leroy A, Gupta V, Tint MT, Shu QOD, Yap F, Lek N, Godfree KM, Chong YS, Lee YS, Eriksson JG, Alvarez MA, Michael N and Wang D. Prospective prediction of childhood body mass index trajectories using multi-task Gaussian processes. Int J Obes. 2025;49, 340–347