Manifold Learning for Neuroimaging Analysis

The scholarship includes home/overseas tuition fees plus a combined maintenance grant and salary equivalent to the Research Councils UK National Minimum Doctoral Stipend (£18,662 for 2023/24) for the first three years followed by fees and a maintenance grant for a further six months. Scholars also receive fee-paid teacher in Higher Education training through the Associate Fellowship Scheme.

Sunday 25 June 2023, 23:59 BST

Eligible applicants should have recently received a good MSc qualification and a first or high 2:1 undergraduate in statistics/computing/neuroimaging/applied mathematics or have substantial recent experience working in a related field.

We are recruiting for a fully funded PhD Studentship that will be awarded as a Graduate Teaching Assistant (GTA) to work with Professor Jian Zhang on a research project titled Manifold Learning for Neuroimaging Analysis.

Project overview: Brain injury often occurs in our community such as sport clubs and veteran clubs, which may have wide-ranging physical and mental effects on victims. This project, considering manifold structures of brain, aims to develop manifold learning tools for diagnosis of brain conditions by integrating scan data generated from non-invasive imaging devices such as structural magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), functional MRI (fMRI) and magnetoencephalography (MEG).

Unlike fMRI, MEG offers excellent potential for research into cognitive brain processes and related regions which are affected by injury in a fine scale. The research field of using fMRI and MEG to diagnose brain injury is rapidly growing. But most of the existing clinical usage of these devices in hospitals and health centres is based on T1-weighted MRI and DTI. The current diagnosis suffers from important limitations: for MEG data, one needs to address the ill-posed problem of inverse imaging; for T1-weighted MRI and DTI data, the existing feature extracting approaches rarely explore structural constraints. Scan data often lie on or near an intrinsic manifold, taking values in an ambient space which is not necessarily Euclidean. There is a growing need for using geometric structures in data analysis. Identifying these underlying manifold structures can help improve the accuracy of denoising and classifying observations. This project will address the above challenges through the development of novel machine learning approaches such as deep learning, reproducing Hilbert kernel-based prediction and nonparametric inference of neural differential equations in an infinite dimensional space. The overall aim of the project is to produce signatures (or markers) that extract robust and clinically useful information from these image scans.

The project provides unique opportunities for cross-disciplinary training in innovative methodologies at the interface of neuroscience and machine learning. This is a computational project, which would suit a student with good mathematical and programming skills in using Python and Matlab, and a keen interest in medical imaging.

Alongside completing your PhD programme of research and development, as a GTA you will normally be expected to work for 200 hours per annum in years 1 to 3, including teaching (maximum 96 contact hours per year) or demonstrating (maximum 130 contact hours per year) and related duties such as marking, preparation and examination. Further details of GTA terms and conditions are here: https: // www. kent.ac.uk/scholarships/postgraduate/terms-and-conditions- gtas

Apply for a Statistics PhD at IPP login screen (kent.ac.uk) and specify the research topic “Manifold Learning for Neuroimaging Analysis”