Modelling and identification of cerebral blood flow control

Co-Supervisor: Professor David Simpson

The project focuses on understanding cerebral blood flow and its physiological control, to improve diagnosis and treatment of brain injuries. The project will adopt a control and system identification approach, and will benefit from the use of real data, clinical collaborations, and links with the international research network CARNet.

The project focuses on blood flow and its physiological control in the brain, in the context of an emerging research trend in cardiovascular sciences set on discovering useful diagnostic information from the dynamic analysis of time series of concurrent physiological measurements. In the context of cerebral blood flow relevant measurements are the electrocardiogram (ECG), continuous blood pressure and cerebral blood velocity, and CO2 levels (capnography).

The objective of this PhD project is to develop new methods to model and to extract diagnostic information from these time series. The aim is faster and better targeted treatments for conditions such as stroke and head injury, resulting in improved brain protection and better outcomes for patients. In this area, the project will benefit from the availability of physics-based models, and of real-world recordings from healthy subjects and clinical patients and includes collaboration with clinical partners at Southampton General Hospital.

The project will adopt a control and system identification approach. Developments of particular interest will be grounded on multivariate systems and system identification with regularisation methods with suitable extension to Bayesian and population formulations, the latter being particularly relevant to biomedical data. This PhD project aims to contribute to the development of these methods and take it in new directions and into new applications in the biomedical field.

The project is linked with the activities of the international research network CARNet.

If you wish to discuss the project informally, please contact Dr Andrea Lecchini-Visintini, Cyber Physical Systems Research Group at [email protected]. 

The ideal candidate will have a degree in Engineering, Statistics, or Applied Mathematics and an interest in applying modelling and estimation techniques for the benefit of patients. A very good undergraduate degree is essential – at least a UK 2.1 honours degree, or its international equivalent.

Early applications are encouraged, applicants will be considered on a rolling basis with funding panel allocations taking place every one/two months.

How To Apply

Apply online: Search for a Postgraduate Programme of Study (soton.ac.uk) Select programme type (Research), 2025/26, Faculty of Engineering and Physical Sciences, next page select “PhD Computer Science”. In Section 2 of the application form you should insert the name of the supervisor

Applications should include:

Research Proposal

Curriculum Vitae

Two reference letters

Degree Transcripts/Certificates to date

General Funding: We offer a range of funding opportunities for both UK and international students, including Bursaries and Scholarships. For more information please visit PhD Scholarships nearmejobs.eu Doctoral College nearmejobs.eu University of Southampton Funding will be awarded on a rolling basis, so apply early for the best opportunity to be considered.