This multidisciplinary PhD project will develop AI and natural language processing approaches to develop structured datasets that can improve prediction of miscarriage and other early pregnancy complications. By unlocking NHS information routinely collected from University College London Hospital (UCLH), the project aims to support earlier diagnosis, personalised risk assessment and improved clinical decision-making.
The successful candidate will work with obstetricians, data scientists and data engineers at the interface of Obstetrics & Gynaecology, Reproductive Medicine, Digital Healthcare, Data Informatics and Trusted AI Research Environments, developing automated pipelines for data extraction, machine learning and artificial intelligence. The project offers opportunities to apply machine learning, natural language processing and data science techniques to large-scale NHS healthcare datasets while working with clinicians, engineers and computer scientists.
The studentship is based at the Digital Environment Research Institute at QMUL, UCLH and Manchester University NHS Foundation Trust (MFT). The successful candidate will receive multidisciplinary training in AI for Healthcare, clinical data science, AI and translational research, while contributing to the development of innovative digital technologies with the potential to improve pregnancy care and outcomes.
Interview Schedule
Interviews will take place on Tuesday 8 September 2026 and 15 September 2026.
Project Team
- Dr Tina Chowdhury – QMUL, Barts Health
- Professor Anna David – UCL & UCLH, Tommy's
- Dr Zara Arain - QMUL, UCLH
- Professor Greg Slabaugh - QMUL
- Professor Steve Harris - UCL & UCLH (CRIO)
- Professor Jenny Myers – MFT
- Professor Anthony Wilson – MFT (CRIO)
- Professor Alexander Heazell - MFT, Tommy's
The project provides a unique opportunity to work within a multidisciplinary team spanning engineering, computer science, clinical medicine and digital health, with strong links to NHS partners and national women's health research networks.
Funding
This PhD studentship is funded by the Tommy's Research to Impact Award supporting the development of AI tools for women's health. The project forms part of a wider programme developing digital technologies to improve prediction and management of miscarriage and pregnancy complications at Tommy's National Centre for Preterm Birth Research and Tommy's National Centre for Miscarriage Research.
Please be sure to quote the reference "SEMS-PHD-736" to associate your application with this studentship opportunity.
