Available
Project number:
2025_47
Start date:
October 2025
Project themes:
Main supervisor:
Professor of Medical Statistics and Complex Intervention Methodology
Co-supervisor:
Dr Ewan Carr
Additional Information:
Leveraging Machine Learning for High-Dimensional Mediation: Explaining Outcomes of Psychological Therapy for Anxiety and Depression
Background
This PhD project aims to enhance our understanding of how treatments for anxiety and depression work by identifying key mechanisms and patient subgroups that respond differently to interventions. Traditional methods for mediation and moderation analysis are not well equipped to handle the high-dimensional data now available, such as genomic data, electronic health records, and digital phenotyping from smartphones and wearable devices. This project will integrate advanced machine learning techniques with traditional approaches, such as structural equation modelling (SEM), to more effectively analyse these datasets and uncover important mechanistic variables.
Novelty and Importance
By applying these cutting-edge methods, the research addresses the growing need to adapt mediation and moderation analyses to fully exploit large-scale, multimodal datasets. The resulting insights will be critical for personalising treatments and improving their effectiveness, particularly for specific subgroups of patients. This approach is novel, as machine learning has rarely been applied to this type of analysis, offering a new way to understand the mechanisms underlying mental health interventions and for whom they work best.
Aims and Objectives
This project will apply state-of-the-art methods to uncover mechanistic variables in high-dimensional, multimodal datasets. The specific aims are to:
1. Identify mediators and moderators of treatment outcomes for anxiety and depression within large-scale datasets.
2. Apply and compare traditional and machine learning-based approaches to mediation and moderation.
3. Share the findings and provide training to make these advanced techniques more accessible for future research.
Ultimately, this PhD project will contribute to advancing personalised mental health treatments, improving their efficacy and targeting through a deeper understanding of underlying mechanisms.
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
