PhD Studentship: Virtual brain networks to guide deep brain stimulation in childhood epilepsy

Overview

A full-time, fixed-term PhD studentship is available at the University of Exeter within the Department of Mathematics and Statistics, funded by the EPSRC Engineering and Physical Sciences Research Council. The research focuses on using mathematical models of brain networks to guide deep brain stimulation for childhood epilepsy. Around a third of children with epilepsy continue to experience seizures despite medication, and while implanted electrical stimulation devices are an expanding option, patient responses vary widely and are currently managed through trial and error over many months. This project aims to use multimodal imaging to develop patient-specific mathematical models that quantify brain network dynamics, helping to predict the efficacy, location, and timing of stimulation to alleviate seizures.

Who can apply

Applicants are welcome from fields including mathematics, physics, engineering, computer science, quantitative neuroscience, or related disciplines. Prior experience in neuroscience or clinical research is not required, as training will be provided. The student will join a research group at Exeter with expertise in the mathematics of brain dynamics and will receive co-supervision from clinicians working in paediatric neurosurgery.

What you get

The studentship provides coverage of tuition fees and an annual stipend of £21,805 per annum for the 2026/7 period. The role is on site in the United Kingdom.

Requirements

The successful candidate will develop methods for constructing patient-specific models from recordings, implement and simulate stimulation within these models, and evaluate how predictions correspond to measured effects using clinical data. The work encompasses challenges in mathematics, statistics, and computational neuroscience, including physiological model development, networked systems dynamics, parameter inference, uncertainty quantification, and the simulation of large dynamical systems.

Key dates

The closing date for applications is 2026-12-08.