About the Project
The early-life microbiome is increasingly recognised as an important component of infant health, yet its relationship with subsequent disease remains difficult to characterise. The microbiome changes rapidly during early life, while important health outcomes may occur at different times and can include hospitalisation and other clinical events. Understanding how changes in the microbiome are related to later health outcomes could provide new opportunities for earlier identification of infants at…
increased risk of disease.
This PhD will develop innovative statistical methods for jointly analysing longitudinal microbiome measurements and clinical outcomes. The project will bring together ideas from Bayesian statistics, longitudinal data analysis, survival analysis and modern statistical learning to develop models that can capture the dynamic relationship between microbiome development and disease risk.
A central aim will be to develop a flexible joint modelling framework that can use repeated microbiome measurements together with information on when clinical events occur. This will allow the research to move beyond analysing microbiome measurements and clinical outcomes separately, providing a more integrated understanding of how microbial changes are associated with subsequent health.
The project will investigate how information from different levels of microbiome data, including microbial taxa and functional characteristics, can be incorporated into statistical models. It will also explore methods for handling the high-dimensional nature of microbiome data and identifying the microbial patterns that provide the most useful information about future health outcomes.
An important component of the research will be dynamic prediction. As additional microbiome samples become available, the models will be used to update an infant's predicted risk of a future clinical event. This could ultimately provide a framework for personalised risk assessment in which predictions evolve as more information about an infant becomes available.
The methodological developments will be motivated by important clinical questions and evaluated through simulation studies and real-world applications. The primary application will use the Baby Biome Study (BBS), which provides longitudinal early-life microbiome measurements together with detailed maternal, neonatal and clinical information and linked health records. Additional neonatal and preterm/NICU datasets will provide opportunities to evaluate the methods in higher-risk populations and different clinical settings.
The project will combine methodological statistics with substantial opportunities for applied research and collaboration with researchers in microbiology, epidemiology and clinical sciences. The successful candidate will have the opportunity to work with large and complex biomedical datasets and contribute to research addressing important questions about infant health.
The project will also develop open and reproducible statistical software, allowing the resulting methods to be used by researchers working with longitudinal microbiome and clinical data more broadly.
The overall goal is to develop a new statistical framework for understanding how early-life microbiome development is related to subsequent health outcomes and for improving the prediction of disease risk. The project offers an opportunity to develop advanced statistical methodology while addressing a timely and important biomedical problem.
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 here.
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 (or international equivalent) in Statistics, Data Science, or a discipline directly relevant to the PhD OR any upper-second class (2:1) honours degree and a Master's degree at merit (or international equivalent) in Statistics, Data Science, or a discipline directly relevant to the PhD. 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.