PhD Studentship: Investigating brain connectivity in Parkinson’s disease using multimodal neuroimaging and deep learning techniques

This project provides an annual stipend of £19,237.

Project advert

Are you driven by a passion for neuroscience and AI technology? Join our cutting-edge research project, where we aim to investigate brain connectivity in Parkinson’s disease using state-of-the-art multimodal neuroimaging and deep learning (DL) techniques. Our goal is to understand the disease by developing modern AI algorithms to identify new imaging markers that correlate with disease severity and progression. This interdisciplinary project, in collaboration with international experts, offers a unique opportunity to contribute to significant healthcare advancements

You will join an established team of experts dedicated to high-quality research in computational neuroscience. Our supervisory team, with extensive experience in computational neuroscience, medical imaging, and deep learning, will guide and support you throughout your research journey. You will have the chance to develop novel DL algorithms.

Additionally, you will benefit from our brand-new facilities housed in the state-of-the-art £117 million Dalton Building, the new home for the Faculty of Science and Engineering. This cutting-edge environment is equipped with outstanding research and social spaces, providing an ideal setting for your work.

Project aims and objectives

• Develop a novel deep learning framework to integrate multimodal neuroimaging data (rs-fMRI, sMRI, and DTI) for investigating brain connectivity.
• Detect neuroimaging markers of neurodegeneration in Parkinson’s disease, providing insights into severity and progression.
• Validate findings using multiple datasets to ensure reproducibility and reliability.
• Correlate structural changes and functional connectivity alterations with the severity and progression of Parkinson’s disease symptoms.

Specific requirements of the candidate

• Applicants should have the following qualifications, skills, knowledge, and experience:
• A minimum of an honours degree at first or upper second class level in a relevant field (such as computer science, biomedical engineering and neuroscience).
• Strong background in programming languages such as Python or MATLAB.
• Good knowledge of machine learning and deep learning techniques.
• Excellent analytical and problem-solving skills.
• Proficiency in medical imaging or computational neuroscience tools (desirable).
• A demonstrated interest in brain research and interdisciplinary research (desirable).

How to apply

Interested applicants should contact Dr. Indranath Chatterjee (main supervisor) for an informal discussion at  mailto:i.chatterjee@mmu.ac.uk.

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

You should also complete the PGR thesis proposal and a Narrative CV (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: 14 October 2024.

Expected start date: January 2025 for Home students and April 2025 for International students.

Please note that Home fees are covered. Eligible International students will need to make up the difference in tuition fee funding.

Please quote the reference: SciEng-2024-Parkinson’s-Disease

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