PhD Studentship: Real Time MRI of Joint and Muscle Mobility in Elite Football Players

This is a full-time, funded PhD opportunity, open to home and overseas students. Only home fees will be covered - eligible overseas students will need to make up the difference in tuition fee funding.

This opportunity provides an annual stipend of the research council minimum rate (set by UKRI) £19,237 for 2024/25.

PROJECT CONTACT

Dr Aneurin J Kennerley

PROJECT ADVERT

The project forms part of a new research and innovation partnership with Manchester United Football Club. The successful candidate will be embedded across the two institutions.

You will develop innovative real-time imaging for achieving unparalleled 3D visualization of lower limb joint movements to inform wider exploration of muscle/bone analytics for automatic injury identification.

You will gain training on the operation of clinical MRI scanners and be responsible for conducting all aspects of the research project, from data collection and analysis to disseminating the findings at scientific conferences and through publication in world-leading journals.

AIMS AND OBJECTIVES

The project aims to deliver AI-empowered innovative dynamic imaging information and key kinetic metric outputs concerning football player mobility for use by Manchester United FC. This approach to real-time imaging will be integrated into an AI imaging analytics package to promote automatic injury detection and aid player diagnosis by the partner medical team.

The key objectives of this project are:

• Development/programming of real-time dynamic imaging of joint motion in the Canon Sequence Development Environment.
• Implementation of AI models to empower hybrid imaging – merging the benefits of low spatial resolution real-time dynamic information with high-resolution structural imaging.
• Integrating dynamic/hybrid imaging into wider software-based AI imaging analytics of muscle asymmetry, fat infiltration, oedema and scaring.
• Use deep learning to deliver automatic injury detection methodologies to reduce false negative assessments.
• Explore the application of developed methodologies after injury to track recovery metrics following training strategies implemented by the partner medical team.

SPECIFIC REQUIREMENTS OF THE PROJECT

Essential criteria

• First-class or upper second-class (2:1) degree (or equivalent) in a relevant discipline (physics, mathematics, computer science, AI, data science or statistics). Strong candidates with sports science, physiotherapy, radiography or sports medicine-related degrees will also be considered.
• Experience in programming, particularly training machine models with at least one of the following (Python, MATLAB, R).
• Ability to critique and analyse scientific evidence, methodology and data.
• Strong interpersonal, communication and organisational skills.
• Proficient with Microsoft Office, specifically Excel.

Desirable criteria

• MSc or research masters in a relevant discipline such as physics, mathematics, computer science, data science or statistics.
• Experience with C++ and XML programming languages.
• Image data analysis or relevant signal processing skills.
• MRI imaging experience at 3 Tesla field strengths.

HOW TO APPLY

Interested applicants should contact Dr Aneurin James Kennerley for an informal discussion.

To apply please complete the online application form for a full-time PhD in Computer Science (or download the PGR application form).

You should complete a standard CV demonstrating how the skills you have, map to the area of research and a cover letter showing why you see this area as being of importance and interest. 

If applying online, please upload your statement in the supporting documents section, or email the application form and statement to PGRAdmissions@mmu.ac.uk.

Closing date 15 July 2024

Expected start October 2024

Please quote the reference: SciEng-AJK-2024-joint-muscle-mobility

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