Available
Project number:
2025_40
Start date:
October 2025
Project themes:
Main supervisor:
Senior Lecturer
Co-supervisor:
Professor Kimberley Goldsmith
Additional Information:
Dynamic Statistical Learning Models for Real-time Prediction and Monitoring Patient Outcomes at Scale
The digital transformation of healthcare has led to an influx of high-dimensional data from sources such as electronic health records, smartphones, and wearable devices. These data streams hold great potential for improving the management of long-term conditions such as diabetes, cardiovascular disease, and mental health disorders. However, fully harnessing this data requires advanced statistical methods to capture the complexity of disease trajectories, symptom fluctuations, and periods of remission and relapse.
This project addresses the growing need for modern statistical and time series approaches to analyse routine and digital health data. Traditional models often fail to account for the variability in patient outcomes, especially in conditions marked by recurrent relapse and remission. By using advanced statistical learning techniques, this project aims to provide personalised insights into symptom dynamics, helping healthcare providers make informed decisions and empowering patients to better understand and manage their symptoms.
The primary objective is to develop, apply, and evaluate dynamic statistical learning models for real-time monitoring and prediction of patient outcomes. Methods will include time series analysis, such as ARIMA and Gaussian Process Regression, and multivariate models to capture interactions between health-related variables. This project will provide practical guidelines for applying these techniques in real-world healthcare settings, contributing to more effective management of long-term conditions.
References available upon request.
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
