[ad_1] Project contact: Dr. Ismail Adeniran Funding info: Home fees (2022/23) included plus an annual stipend paid at the UKRI rate (£
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Project contact: Dr. Ismail Adeniran
Funding info:
Home fees (2022/23) included plus an annual stipend paid at the UKRI rate (£17,668 for 2022/23). Overseas students will need to make up the difference in tuition fees
Mode of study: Full time
Eligibility: Open to home and overseas students.
Key dates:
Closing date: 18th June 2023
Expected start: October 2023
Project summary
The project involves the use of reinforcement learning, optimal control and computer vision techniques to understand the causal link between genotype and phenotype in Hypertrophic Cardiomyopathy. Hypertrophic Cardiomyopathies (HCM) are a group of familial heart diseases and occur in about one in every 500 births [1,2]. They are the most common cause of sudden cardiac death in young athletes and adolescents. The mutations that cause HCM cause the walls of the heart to thicken and stiffen making it harder for the heart to pump blood out of the heart and around the body. The consequences range from fainting, irregular heart rhythms, blood flow obstruction and sudden death.
Aims and objectives
The overarching goal is to use a unique combination of reinforcement learning, optimal control and computer vision techniques, and algorithms to develop intelligent models to reproduce experimentally observed HCM behaviour at the subcellular, cellular and tissue levels. This will be used (1) to understand the causative link between genotype and phenotype in HCM and other fundamental underlying mechanisms, and (2) to identify possible therapeutic pathways to improve patient conditions and quality of life.
Specific requirements of the project
- A very good undergraduate degree (at least a UK 2:1 honours degree) in a quantitative discipline such as engineering, physics, computer science or mathematics.
- A good mathematical background and programming skills in at least one of C/C++, Fortran, Rust, Java, Julia, Nim, Zig or Python.
- Experience of machine learning and numerical methods will be beneficial.
- A keen interest in high-impact research work at the interface of physics, engineering, computer science and medicine.
How to apply (include weblink)
Interested applicants should contact Dr. Ismail Adeniran for an informal discussion.
To apply you will need to complete the online application form for a full-time PhD in Computing and digital technology (or download the PGR application form). You should also complete the PGR thesis proposal (supplementary information) form addressing the project’s aims and objectives, demonstrating how the skills you have maps to the area of research and 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 12th June 2023. Expected start date October 2023.
Please quote the reference: SciEng-IA-2023-machine-learning
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