DPhil Studentship in Control and Neuroscience: Model-based Prediction and Control of Seizure-like Events (October 2027 entry)

A 3.5-year DPhil studentship in Oxford's Control Group, supervised by Dr Thiago B. Burghi, developing data-driven, mechanistically interpretable models that predict seizure-like events in living neural networks from extracellular activity. Candidates may draw on control theory, machine learning and computational neuroscience; Recurrent Mechanistic Models, which combine state-space systems and artificial neural networks, are a suggested starting point. Models are validated on high-density multielectrode recordings from mouse brain slices, and the project then explores model-based closed-loop stimulation protocols. The studentship is part of Dr Burghi's Royal Society University Research Fellowship "Data-driven closed-loop control of living neural rhythms", in collaboration with Prof Ed Mann's laboratory (Department of Physiology, Anatomy and Genetics). The application deadline is noon on 2 December 2026, for an October 2027 start; applicants quote studentship code 27ENGCO_TB.

Full award (home fees plus stipend) for home students; there may be flexibility to support international students, who should contact the supervisor first. Applicants need a first-class or strong 2:1 degree in engineering, computer science, physics or mathematics and excellent English; experience in control theory or system identification, machine learning and scientific computing, computational neuroscience and biophysical modelling, programming, or real-time hardware is desirable. Applications go through the University's graduate application form for the DPhil in Engineering Science, quoting 27ENGCO_TB.