PhD: Acoustic speech and machine learning in neuro disorders

PhD project at Edinburgh in acoustic speech and machine learning studying voice based biomarkers for diagnosis and monitoring in neurodegenerative disorders

This project is one of seven project choices on offer as part of the studentship scheme of the Euan MacDonald Centre for Motor Neuron Disease Research. The Euan MacDonald Centre is a multi-disciplinary network of motor neuron disease (MND) researchers across Scotland. Projects are being advertised now for a start date in Autumn 2026 at the University of Edinburgh, University of Dundee or University of St Andrews.

Euan MacDonald Centre PhD students join our network of >250 MND researchers across Scotland and will have the opportunity to meet people living with MND, participate in academic and public engagement events led by the Centre.

To view all the projects offered under this scheme please visit www.euanmacdonaldcentre.org/phd-scheme.

Project description

This project is based at the University of Edinburgh.

There is an urgent need for scalable, non-invasive, low burden, and cost-effective biomarkers for diagnosis and monitoring of neurodegenerative disorders including motor neuron disease (MND) and dementia. Speech is an attractive candidate which can be acquired remotely promoting decentralised clinical and research assessments.

Our inter-disciplinary team of clinicians, speech scientists, programmers, and experts in artificial intelligence (AI) and machine learning (ML) has pioneered implementation of an App co-produced with our patient advisory group for acquisition of voice data from individuals with MND, dementia, Parkinson’s, and multiple sclerosis.

We have established a globally leading archive of high-quality voices from deeply clinically annotated longitudinal cohorts. Application of advanced AI/ML approaches has yielded preliminary data demonstrating the utility of voice in detection and monitoring of NDDs.

The successful applicant will work on novel algorithm development and refinement for differential diagnosis of NDDs, prognostication, evaluating determinants of phenotypic heterogeneity, disease monitoring, and language agnostic models.