About this position
Application deadline: All year round
This 3.5-year PhD project is fully funded and home students are eligible to apply. The successful candidate will receive an annual tax-free stipend set at the UKRI rate (£21,805 for 2026/27) and tuition fees will be paid. We expect the stipend to increase each year. The start date is October 2026.
We recommend that you apply early as the advert may be removed before the deadline.
In many applications such as biological sciences, social science, and engineering, we encounter high-dimensional observations. Bayesian approach can provide a flexible modeling framework for underlying structures in high dimension such as underlying covariance structure, conditional dependency graphs etc. With the change in data-generating mechanism, these high-dimensional structures may change with time, where the change can depend on latent factors or variables.
These projects will focus on developing a comprehensive Bayesian learning framework for this broad class of problems while focusing on specific applications. The goal would be to develop computationally efficient and scalable Bayesian learning methodologies with practical applications and establish relevant theoretical properties.
Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline.
To apply, please contact Dr Nilabja Guha - nilabja.guha@manchester.ac.uk . Please include details of your current level of study, academic background and any relevant experience and include a paragraph about your motivation to study this PhD project.
£21,805 annual tax-free stipend set at the UKRI rate and tuition fees will be paid
Funding
GBP 21,805 per year
How to apply
- Read the full advert on the source site — it carries the authoritative terms.
- Email the contact below with your CV and a short, specific message. See Emailing Professors.
- Prepare your SOP, CV, transcripts and referees before the deadline.
- Apply through the university's own portal. Never pay a fee to a third party.