Doctorat / PhD — Research Studentship in Control and Neuroscience: Model-based Prediction and Control of Seizure-like Events

Doctorat / PhD Département : Department of Engineering Science Type de contrat : Full Time,Fixed-Term/Contract Date limite de candidature : 2026-12-02T00:00:00+00:00

3.5-year D.Phil. studentship Supervisor: Dr Thiago B. Burghi This project will develop data-driven models for predicting seizure-like events in living neural networks. Such events can be induced in brain slices and share many features with epileptic seizures in the human brain. The underlying challenge is to obtain predictive models of extracellular neural activity that remain interpretable from the mechanistic viewpoint of biological neuromodulation. The student will have the freedom to develop their own modelling approaches, drawing on ideas from control theory, machine learning and computational neuroscience. A promising starting point is provided by Recurrent Mechanistic Models (RMMs), which combine state-space systems and artificial neural networks to capture neural dynamics. Models will be developed and validated using high-density multielectrode recordings from mouse brain slices. The project will then explore the implementation of model-based stimulation protocols using closed-loop multielectrode arrays. The studentship forms part of Dr Burghi’s Royal Society University Research Fellowship, “Data-driven closed-loop control of living neural rhythms” and will be based in Oxford’s Control Group. Research will be carried out in collaboration with Prof Ed Mann’s laboratory in the Department of Physiology, Anatomy and Genetics, with opportunities to work with developers of multielectrode technology. The student will develop expertise in system identification, machine learning and neuroengineering, contributing to fundamental research on epileptiform dynamics and the development of principled closed-loop neurostimulation. Eligibility This studentship is funded through the Department of Engineering Science at the University of Oxford and is open to home students (full award – home fees plus stipend). There may be flexibility to support international students. If you are an international student and want to apply for this studentship, please contact the supervisor to see whether the flexibility might be available for you. Award Value Course fees are covered at the level set for UK students (at least £10,940 p.a.). The stipend (tax-free maintenance grant) is at least c. £21,805 p.a. for the first year, and at least this amount for a further two and a half years. Candidate Requirements Prospective candidates will be judged according to how well they meet the following criteria: A first class (or strong 2:1) degree in any of Engineering, Computer Science, Physics, Mathematics. Excellent English written and spoken communication skills. Experience in one or more of the following areas is desirable: Control theory and/or system identification Machine learning and scientific computing Computational neuroscience and biophysical modelling Programming and software development Real-time hardware Application Procedure Informal enquiries are encouraged and should be addressed to Dr Thiago Burghi at [email] . Candidates must submit a graduate application form and are expected to meet the graduate admissions criteria. Details are available on the course page of the University website . Please quote 27ENGCO_TB in all correspondence and in your graduate application. Application deadline: noon on 2 nd December 2026 (In line with the December admissions deadline, set by the University) Start date: October 2027 £21,805 p.a.