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Statistical machine learning for computational neuroscience

   Centre for Accountable, Responsible and Transparent AI

   Applications accepted all year round  Competition Funded PhD Project (Students Worldwide)

About the Project

This project will focus on signal processing in neuroscience applications using state-of-the-art statistical machine learning techniques. The image modalities include EEG/MEG/fMRI and the application covers clinical application (dementia, stroke, glioma), brain computer interaction (BCI), and smart prosthetics etc. Specifically, multi-sensory time-series signals will be collected and modelled under a Bayesian framework. Techniques such as Gaussian processes, recurrent neural networks and probabilistic sampling methods will be used to perform parameter estimation, decision making, and uncertainty quantification.

The project is expected to contribute to the algorithmic/theoretical development of interdisciplinary computational neuroscience research, as well as to improve the reliability and explainability of biomedical diagnostics and healthcare applications using AI techniques. The expected outcomes of this project may include: (a) a more profound understanding of the aetiology of neurological diseases and cortical activities from a Bayesian statistical learning perspective; (b) application-based software prototypes for digital health, disease management, or BCI applications.

The successful applicant will have technological and academic support from an interdisciplinary supervisory team composed of experts from the Department of Computer Science at the University of Bath and the Department of Clinical Neurosciences at the University of Cambridge. Successful applicants will be offered doctoral-level training in cutting-edge statistical machine learning methods and practical multi-channel time-series data processing techniques. They will also be encouraged to publish academic papers and to attend international conferences in machine learning, psychology, or BCI research fields.

This project is associated with the UKRI Centre for Doctoral Training (CDT) in Accountable, Responsible and Transparent AI (ART-AI). We value people from different life experiences with a passion for research. The CDT's mission is to graduate diverse specialists with perspectives who can go out in the world and make a difference.

Applicants should hold, or expect to receive, a first or upper-second class honours degree in computer science, information engineering, biomedical engineering, cognitive neuroscience, statistics, mathematics, or a closely related discipline. A master level qualification and knowledge of machine learning and cognitive neuroscience would be advantageous. Prior knowledge in machine learning is desirable, but not required.

Informal enquiries about the research should be directed to Dr Xi Chen: .

Formal applications should be accompanied by a research proposal and made via the University of Bath’s online application form. Enquiries about the application process should be sent to .

Start date: 2 October 2023.

Funding Notes

ART-AI CDT studentships are available on a competition basis and applicants are advised to apply early as offers are made from January onwards. Funding will cover tuition fees and maintenance at the UKRI doctoral stipend rate (£17,668 per annum in 2022/23, increased annually in line with the GDP deflator) for up to 4 years.
We also welcome applications from candidates who can source their own funding.

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