The University of Manchester — Department of Mathematics
Supervisors: Dr Taban Baghfalaki, Dr Michael Anderson, Dr Rosa Parisi
Applications accepted all year round
Competition Funded PhD Project (Students Worldwide)
Tags: Manchester, United Kingdom, Applied Statistics, Data Analysis, Data Science, Medical Statistics, Statistics
About the Project
This PhD project will develop statistical methodology for jointly modelling longitudinal measurements and time-to-event outcomes using large-scale real-world healthcare data. Longitudinal records collected repeatedly over time provide information about how a patient's health evolves, while time-to-event outcomes capture clinically important events such as disease progression, hospitalisation or death. Joint modelling provides a principled framework for analysing these two sources of information simultaneously, but existing approaches can face substantial computational and methodological challenges when applied to large and complex healthcare datasets.
The main objective is to develop flexible and computationally efficient joint modelling frameworks suitable for large-scale longitudinal and time-to-event data. The research will investigate Bayesian and likelihood-based approaches, appropriate dependence structures between longitudinal and survival processes, and methods for handling complex observation patterns and heterogeneous patient trajectories. Methodological developments will be evaluated through theoretical investigation and extensive simulation studies, assessing statistical properties, computational efficiency and predictive performance.
A key component of the project will be the application of the developed methodology to large-scale healthcare datasets, including the Clinical Practice Research Datalink (CPRD) Aurum and the UK-PBC cohort. These applications will demonstrate the practical value of the proposed methods and provide opportunities to address clinically relevant questions involving longitudinal and time-to-event outcomes. The project will also investigate dynamic risk prediction and compare predictive performance with established joint modelling approaches.
The student will receive training in Bayesian statistics, longitudinal data analysis, survival analysis, joint modelling, computational statistics and statistical programming, particularly using R. They will develop skills in simulation studies, large-scale data analysis, reproducible research, scientific writing and presentation. Training will be provided through regular supervision, research group activities, seminars, workshops and relevant doctoral training courses. The student will be encouraged to disseminate their work through peer-reviewed publications and national and international conferences.
Expected outcomes include new statistical methodology for large-scale joint modelling, efficient computational tools, applications to real-world healthcare data, and improved approaches for dynamic risk prediction.
This project is expected to start in September 2027.
Before you apply:
We strongly recommend that you contact the supervisors for this project before you apply.
How to apply:
To be considered for this project you must complete a formal application through our online application portal. If you already have an applicant account this link will directly open an application for PhD School of Natural Sciences Scholarships. If you don't already have an applicant account, please follow the instructions linked from the application portal.
When applying, please specify the full title and supervisor/s of the project, details of your previous study, and names and contact details of two referees. You must also upload a Supporting Statement describing your motivation to apply to the project, your CV and transcripts of awarded and in-progress university qualifications. Please note late or incomplete applications will not be considered.
Equality, diversity and inclusion are fundamental to the success of The University of Manchester and central to all our activities. A diverse research community strengthens creativity, productivity and quality, while increasing the societal and economic impact of our work. We welcome applicants from all career paths, backgrounds and sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation or transgender status.
We welcome applications from candidates returning to study after a career break or experience in other roles. Flexible study arrangements may be available, including part-time study at 50%, 60% or 80%, subject to the requirements of the project and funder.
Eligibility: The standard academic entry requirement for this PhD is an upper second-class (2:1) honours degree in Statistics / Data Science or a discipline directly relevant to the PhD (or international equivalent) OR any upper-second class (2:1) honours degree and a Master's degree at merit in Statistics / Data Science or a discipline directly relevant to the PhD (or international equivalent). Previous research experience in statistical programming, particularly R or Python is desirable.
This project will remain open until filled.
If your application is submitted by 1st November 2026, you can expect a decision by 18th December 2026.
If your application is submitted by 15th January 2027, you can expect a decision by 30th March 2027.
Self or externally funded students can also be considered for this project.
FSE_SoNS
Funding Notes
Funding covers tuition fees, an annual stipend at the UKRI minimum rate (currently £21,805 per year), and a research training support grant of up to £2,500 per year for the full 3.5-year programme.