Advert
Advert

Fully Funded PhD: Real time MRI of joint and muscle mobility in elite football players

  • DeadlineDeadline: 15 July 2024.
  • North West, All EnglandNorth West, All England

Description

The project forms part of an exciting new research and innovation partnership between Manchester Metropolitan University and Manchester United Football Club.

The successful candidate will be embedded across the two institutions, thereby undertaking developmental research on two in-house 3 Telsa magnetic resonance imaging (MRI) scanners. This project aims to develop novel MRI methodologies and analysis software to drive improved health and performance outcomes for players.

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.

The successful candidate will have programming experience, in particular training AI models, preferably in an applied imaging/sport setting. In post, you will gain comprehensive training on the operation of clinical MRI scanners. The successful candidate will 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 (in particular lower limb joint/muscle/bone) for use by Manchester United Football Club. This state-of-the-art 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.

You will meet this aim by completing the following key objectives:

  • Development/programming of real-time dynamic imaging of joint motion (>70 frames per second - based on multi-band radial k-space sampling and sparse reconstruction) 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 the developed methodologies after injury onset to track recovery metrics following training strategies implemented by the partner medical team.

Your research and development in this imaging space will have an immediate impact on a world-leading football club. In addition, the techniques you devise will find wider application across musculoskeletal research frameworks.

Entry Requirements

  • A first-class or upper second-class (2:1) degree (or equivalent) in a relevant discipline such as 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, in particular training machine models with at least one of the following (Python, MATLAB, R).
  • An ability to critique and analyse scientific evidence, methodology and data.
  • Strong interpersonal, communication and organisational skills.
  • Proficient with Microsoft Office, specifically Microsoft 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 (a.kennerley@mmu.ac.uk) for an informal discussion.

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

You should also 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, you will need to 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 date: 7 October 2024

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

Who is eligible to apply?

Open to both home and overseas students. Please note that only home fees will be covered - eligible overseas students will need to make up the difference in tuition fee funding.

Learn more about Manchester Metropolitan University

Where is Manchester Metropolitan University?

Videos