Available
Project number:
2025_28
Start date:
October 2025
Project themes:
Main supervisor:
Senior Lecturer
Co-supervisor:
Dr Raquel Iniesta, Reader in statistical learning for precision medicine
Additional Information:
Individualised prediction models for preterm infants which leverage the 'bumpiness' of their clinical course using high temporal resolution routine health data
Background Extremely preterm infants spend their first three months of life on the neonatal intensive care unit. They are at high risk of later neurodevelopmental conditions like ADHD, but this is difficult to predict at an individual level. Prognostic models often utilise ‘static’ predictors like their weight at birth. However, their intensive care admission is associated to rich longitudinal data, which could characterise the non-linear ‘bumpiness’ of their clinical trajectory. For example, they may gradually get well, but with two episodes of sepsis which decelerate their progress each time. Novelty & Importance To understand neurodevelopmental risk in extremely preterm infants requires to model the dynamism of their clinical course. In a highly novel piece of work, we have previously shown the feasibility of mining their history of painful procedures [1], which is considered very important by these infants’ families [2], not least because these disturb sleep [3]. Here you will extend this innovative approach by integrating multiple other relevant longitudinal data. A second novel aspect is our research group’s interest in biomedical data across multiple nested time scales, including a strand of research on physiological monitoring of signals such as the electroencephalograph (EEG), which vary millisecond by millisecond. Aims & Objectives You will test the feasibility of retrieving longitudinal data like body weight from routine health records, integrating these with single in-depth measures like EEG, and whether applying machine learning to these metrics to create individualised predictive models offers improved prognostic value [4]. References[1] Laudiano-Dray, M. P. et al. Quantification of neonatal procedural pain severity: a platform for estimating total pain burden in individual infants. 2020. Pain. [2] Whitehead. Families and patient involvement in designing a project to analyse routine clinical paediatric EEG recordings for research purposes. https://osf.io/epk4g/ [3] Georgoulas et al. Sleep-wake regulation in preterm and term infants. 2021. Sleep. [4] Salazar de Pablo, G et al. Individualized prediction models in ADHD: A systematic review and meta-regression. 2024. Molecular Psychiatry.
We are now accepting applications for 1 October 2025
How to apply
Candidates should possess or be expected to achieve a 1st or upper 2nd class degree in a relevant subject including the biosciences, computer science, mathematics, statistics, data science, chemistry, physics, and be enthusiastic about combining their expertise with other disciplines in the field of healthcare.
Important information for International Students:
It is the responsibility of the student to apply for their Student Visa. Please note that the EPSRC DRIVE-Health studentship does not cover the visa application fees or the Immigration Health Surcharge (IHS) required for access to the National Health Service. The IHS is mandatory for anyone entering the UK on a Student Visa and is currently £776 per year for each year of study. Further detail can be found under the International Students tab below.
Next Steps
- Applications submitted by the closing date of Thursday 30 January 2025 will be considered by the CDT. We will contact shortlisted applicants with information about this part of the recruitment process.
- Candidates will be invited to attend an interview. Interviews are projected to take place in March 2025.
- Project selection will be through a panel interview chaired by either Professor Richard Dobson and Professor Vasa Curcin (CDT Directors) followed by informal discussion with prospective supervisors.
- If you have any questions related to the specific project you are applying for, please contact the main supervisor of the project directly.
For any other questions about the recruitment process, please email us at
