The University of Manchester | Department of Mathematics
The Department of Mathematics at the University of Manchester is inviting applications for a fully funded PhD studentship in Bayesian modeling of high-dimensional structural data.
The project will develop scalable Bayesian learning methods for complex high-dimensional datasets, with potential applications across areas such as biological sciences, social sciences, and engineering.
Key Information
- Degree: PhD
- Location: Manchester, United Kingdom
- Study mode: Full-time
- Funding eligibility: UK home students
- Duration: 3.5 years
- Start date: October 2026
- Annual stipend: £21,805 tax-free for 2026/27, at the UKRI rate
- Tuition fees: Fully covered
- Advert closing date: 28 October 2026
- Application timing: Applications accepted year-round, although early application is strongly recommended
- Supervisor/contact: Dr Nilabja Guha
Research Focus
High-dimensional datasets are increasingly common in fields such as biology, social science, and engineering. These datasets may contain complex underlying structures, including:
- Covariance structures
- Conditional dependency networks
- Graphical relationships
- Latent factors and variables
- Structures that evolve over time
Bayesian methods provide a flexible framework for identifying, modelling, and interpreting these structures.
This PhD project will focus on developing a comprehensive Bayesian learning framework for high-dimensional structural data, particularly in settings where the underlying structure may change over time or in response to latent factors.
The research will aim to develop methods that are:
- Computationally efficient
- Scalable to high-dimensional datasets
- Applicable to real-world problems
- Supported by rigorous theoretical properties
The successful candidate will have opportunities to work at the intersection of Bayesian statistics, high-dimensional inference, statistical learning, computational statistics, and applied data science.
Eligibility
Applicants should have, or expect to obtain:
- At least a 2:1 UK honours degree, or
- A master’s degree, or
- An equivalent international qualification
in a relevant science, mathematics, statistics, engineering, or related discipline.
A strong background in quantitative methods, statistics, probability, mathematics, or computational methods would be relevant to the project.
Funding
This is a fully funded 3.5-year PhD studentship for eligible home students.
The funding package includes:
- Full tuition fee coverage
- A tax-free annual stipend of £21,805 for 2026/27
- Stipend support at the UKRI rate
- Expected annual increases in the stipend
How to Apply
Prospective applicants should contact:
Dr Nilabja Guha
Email: nilabja.guha@manchester.ac.uk
The initial enquiry should include:
- Current level of study
- Academic background
- Relevant academic, research, or professional experience
- A short paragraph explaining your motivation for undertaking this PhD project
Applicants are encouraged to apply early, as the advertisement may be withdrawn before the stated closing date.
