Don't miss our weekly PhD newsletter | Sign up now Don't miss our weekly PhD newsletter | Sign up now

  Statistical machine learning for computational neuroscience

   Centre for Accountable, Responsible and Transparent AI

This project is no longer listed on and may not be available.

Click here to search for PhD studentship opportunities
  Dr Xi Chen  No more applications being accepted  Self-Funded PhD Students Only

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.

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: [Email Address Removed].

Formal applications should be accompanied by a research proposal and made via the University of Bath’s online application form. Further information about the application process can be found here.

Start date: Between 8 January and 30 September 2024.

Biological Sciences (4) Computer Science (8) Engineering (12) Mathematics (25) Medicine (26)

Funding Notes

We welcome applications from candidates who can source their own funding. Tuition fees for the 2023/4 academic year are £4,700 (full-time) for Home students and £26,600 (full-time) for International students. For information about eligibility for Home fee status:

How good is research at University of Bath in Computer Science and Informatics?

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities
Search Suggestions
Search suggestions

Based on your current searches we recommend the following search filters.