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Over the past few years, medical imaging has grown in popularity and emerged as the most effective tool for addressing global healthcare challenges. It remains unclear, however, why some patients experience significant illness symptoms despite normal lung imaging. Our research aims to tackle the burden of lung diseases such as COVID-19, COPD and Asthma using medical image-based computational modelling and a lung image analysis AI.
The computational model will elucidate the dynamic behaviour of respiratory flows, while the AI will interpret the lung images generated by the computational model and functional imaging modalities. These state-of-the-art tools supporting diagnostic assessment will enable clinicians to interpret lung images accurately, support treatment planning and may lead to new therapeutic interventions. This multidisciplinary research team project will require significant computational programming, modelling, and data analysis in close collaboration with medical imaging and clinical science research experts. The field of this project encompasses the areas of:
- Mathematical/computational modelling
- Software development
- Artificial intelligence
- Medical imaging.
Any student interested in pursuing a PhD in one of the above areas, or more broadly in Computational Biomedicine is welcome to apply.
Primary supervisor: Minsuok Kim
Entry requirements for United Kingdom
Applicants should have, or expect to achieve, at least a 2:1 Honours degree (or equivalent) in Biomedical Engineering, Mechanical Engineering, Computer Science, Physics or a related subject. A relevant Master’s degree and/or experience in one or more of the following will be an advantage: Computational modelling, AI algorithm, Software development and Medical imaging.
English language requirements
Applicants must meet the minimum English language requirements. Further details are available on the International website.
HOW TO APPLY
All applications should be made online. Under programme name, select Mechanical and Manufacturing Engineering. Please quote the advertised reference number P2SAM22-12.
To avoid delays in processing your application, please ensure that you submit the minimum supporting documents.