PhD studentship in Statistical Modelling of respiratory disease trajectories

Applications are invited for a fully funded full-time PhD research position at the National Heart and Lung Institute, Imperial College London, UK. The PhD position will focus on the application of modern statistical techniques to investigate the development of respiratory diseases over time.

This project aims to utilise statistical models for longitudinal data to identify disease trajectories that can help us to deepen our understanding of the causal pathways and to translate this knowledge into personalized prevention and management strategies.

The analyses and results will be presented in international conference and will be published in high impact journals. The student will receive training on use the use of statistical and machine learning approaches to analyse cross-sectional and longitudinal data obtained from birth cohort studies and in order to develop diagnosis and treatments recommendations and will be encouraged to develop his/her own interpretational algorithm.

The successful candidate will be based at the National Heart and Lung Institute, Imperial College London, but will have the opportunity to also work within the multidisciplinary research group working on the MRC-funded project  ‘Early Life Exposures and Development of Noncommunicable diseases in Adolescence: The Drakenstein’s Child’s Health study”, a unique birth cohort in South Africa. The candidate will be working with longitudinal data on respiratory health and lung function data, specifically focussing on Lung Function trajectories and sensitisation. This exciting collaboration creates a unique opportunity for scientific excellence and career development in medical statistics and health data analytics.

The Drakenstein Child Health Study (DCHS) is a multi-year birth cohort study following 1,000 mother child pairs to investigate the epidemiology, aetiology and risk factors for childhood lower respiratory illness and the impact on child health, including the development of chronic respiratory disease. The study aims to investigate the role and interaction of potential risk factors covering 7 areas (environmental, infectious, nutritional, genetic, psychosocial, maternal and immunological risk factors) that may impact on child health. Mothers were enrolled at 20-28 weeks gestation and children followed until they reach ten years of age. This will provide an innovative, longitudinal assessment of a range of clinical, molecular, environmental and socioeconomic variables impacting on childhood respiratory illness and the evolution of chronic disease in a low and middle-income country setting. This study takes place at three sites in the Drakenstein sub-district near Cape Town, South Africa.

The PhD student will be supervised by Prof Adnan Custovic (NHLI) Professor Heather Zar, Professor Di Gray (University of Cape Town) and Dr Sara Fontanella (NHLI).

Application Process

To apply, please submit a one-page personal statement detailing your academic background and research interests, a CV and contact information of two professional/academic references to Dr Fontanella via email (s.fontanella@imperial.ac.uk). Informal inquiries should also be directed to Prof Custovic or Dr Fontanella. The studentship is expected to start in October 2024.

Eligibility

The ideal candidate should have strong skills in complex data analysis, an excellent academic track record and have obtained an undergraduate degree at 2:1 level or higher and, normally, a Master’s degree with merit or higher (or non-UK equivalents) in Statistics, Data Science or another relevant field. Eligible candidates should be self-motivated, proactive, have excellent oral/written communication abilities and be able to work well within a multidisciplinary translational research team.

Funding notes

This position is available as a fully funded PhD studentship, including 3 years of tuition fees (home rate only) and provides a 3-year, tax-free stipend of £20,622

Please note that candidates must fulfil the college admission criteria.

Application deadline  16^th June 2024