About this position
This 3.5-year PhD project is fully funded by The Department of Mechanical and Aerospace Engineering; students who are eligible to pay tuition fees at the Home rate 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.
We recommend that you apply early as the advert may be removed before the deadline. The start date is October 2026 or January 2027.
We recommend that you apply early as the advert may be removed before the deadline.
Phase-contrast magnetic resonance imaging (Flow MRI) is a powerful and non-invasive imaging technique that measures blood flow in time and space. It provides vital insights into key metrics for cardiovascular disease diagnosis and management, such as velocity, wall shear stress, and turbulence. However, its clinical application is currently severely limited by high noise (low signal-to-noise ratio) and low spatial and temporal resolution.
This project aims to overcome these critical limitations by applying advanced machine learning techniques to denoise and improve spatial resolutions. You will run high-fidelity computational fluid dynamics (CFD) simulations of blood flow through arteries and develop a cutting-edge super-resolution framework using convolutional neural networks (CNNs). The ultimate goal is to vastly improve the reliability of haemodynamic metrics derived from Flow MRI, enabling their direct use in clinic to support cardiovascular disease management.
Expected Outcomes
- Develop a novel machine learning framework for MR image super resolution using high fidelity CFD data.
- Validate the software using MRI scans of arterial flow phantoms.
- Collaborate directly with clinicians to maximise the translational and clinical impact of the research.
Training Opportunities
The student will benefit from working alongside a multidisciplinary team of engineers, scientists and clinicians. There will be opportunities for research visits to our collaborators in Europe. Training can be provided in computational fluid dynamics and machine learning.
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, mathematics or engineering related discipline.
- Demonstrated excellence in fluid mechanics, machine learning, or both.
- Experience in programming (e.g., Python, MATLAB, C++, etc).
- Strong written and verbal communication skills.
Please contact the main supervisor, Dr Emily Manchester - emily.manchester@manchester.ac.uk .
Please send your CV and a paragraph about your motivation to study this PhD project to Dr Emily Manchester emily.manchester@manchester.ac.uk
£21,805 tax-free stipend and tuition fees will be paid (at home rate)
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.