Available
Project number:
2025_31
Start date:
October 2025
Project themes:
Main supervisor:
Professor of Maternal and Perinatal Science
Co-supervisor:
Dr Alessandra Vigilante
Additional Information:
Harnessing multiomics and deep clinical phenotyping to develop predictive models for preterm birth
Background
Every expectant parent anticipates a healthy pregnancy and baby. However, approximately 20% of pregnancies in the UK are affected by complications such as spontaneous preterm birth (sPTB), hypertension, gestational diabetes mellitus (4-5%), and fetal growth restriction (up to 10%). Rates of these complications are even higher in some regions of the world and in women with pre-existing medical conditions. Our approach is to integrate clinical and biological data from longitudinal pregnancy-child cohorts to gain greater insight into the biological mechanisms and maternal exposures that result in such pregnancy complications. This strategy is key to developing prediction tools, preventative therapies and treatments that benefit both the mother and child.
Novelty & Importance
Clinical management tools and therapies to prevent/treat risk of spontaneous preterm birth are extremely limited. We previously established a statistics-based algorithm integrated into a smart phone app for identifying mid trimester risk of preterm birth (Quipp app). We are now focussing on incorporating new additional information and biomarkers into the app.
Aims & Objectives
This PhD project is embedded within a large programme of preterm birth research lead by Professor Tribe at King’s College London. The overarching aim is to integrate pregnancy and child outcome clinical data with early pregnancy multiomics and biomarker UK pregnancy dataset to better predict risk of preterm birth. This will be achieved using bioinformatics/machine learning approaches. This will be validated and further developed using additional data from an ongoing study based in three countries in Africa (PRECISE cohort).
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
