A 3-year PhD studentship funded by the NIHR HealthTech Research Centre in Paediatrics and Child Health (NIHR HRC PCH) is available at the UCL Great Ormond Street Institute of Child Health (GOS ICH) in collaboration with Great Ormond Street Hospital for Children NHS Foundation Trust. The studentship forms part of the NIHR HRC PCH Long-term Conditions in Childhood research theme, which supports the development of innovative technologies to improve healthcare for children and young people.

Background

Epilepsy is one of the most common serious neurological conditions in childhood. Electroencephalography (EEG) plays a central role in diagnosis, monitoring, and presurgical evaluation, particularly in children with drug-resistant epilepsy. However, manual review of long-duration EEG recordings is time-consuming and resource-intensive. Recent advances in machine learning and artificial intelligence offer the potential to automate aspects of EEG interpretation, improving efficiency and consistency while supporting clinical decision-making. This project will develop a large, anonymised paediatric EEG database and apply state-of-the-art AI techniques to detect seizures, predict seizure occurrence, and model clinically relevant outcomes such as suitability for epilepsy surgery.

Hypothesis/Aims

To develop clinically interpretable decision-support tools to assist EEG reporting and epilepsy management.

Outputs

This project will:

  • Develop a structured paediatric EEG database of long-duration recordings from children evaluated for epilepsy at Great Ormond Street Hospital, linked with relevant clinical metadata.
  • Develop EEG preprocessing and annotation pipelines, including artefact detection and semi-automated labelling of seizure and non-seizure periods.
  • Implement AI methods for seizure detection and prediction, including deep learning approaches such as convolutional, recurrent, and transformer-based models.
  • Integrate EEG features with clinical variables to develop predictive models for surgical candidacy and post-surgical seizure outcomes.
     

About you

Applicants should hold, or expect to obtain, a Master’s degree (or equivalent experience) in biomedical engineering, neuroscience, computer science, data science, or a related discipline.

Essential skills include:

  • Strong quantitative and analytical skills
  • Programming experience (e.g. Python)
  • Interest in machine learning, signal processing, or biomedical data analysis

Experience with EEG analysis, machine learning, or deep learning would be advantageous.

Applicants must meet the UCL Child Health MPhil/PhD entry requirements, including English language requirements where applicable.

What we offer

The studentship:

  • Provides a starting stipend of £23,805 per annum, increasing in line with UKRI/UCL stipend rates.
  • Covers the cost of UCL tuition fees based on the UK (Home) rate.
  • Provides £2,200 for travel and £6,000 for consumables across the three years.

The student will be based at the UCL Great Ormond Street Institute of Child Health and will work closely with clinicians and researchers at Great Ormond Street Hospital, providing a strong interdisciplinary environment at the interface of clinical neuroscience, biomedical data science, and AI.

Only applicants eligible for UK/Home fees status may apply.

Funding is not provided for paid parental or medical leave; unpaid interruptions may be requested.

How to apply

Enquiries regarding the post can be made to Dr Gerald Cooray – g.cooray@ucl.ac.uk or Professor J. Helen Cross – h.cross@ucl.ac.uk

To apply, please send a current CV including the contact details of two professional referees as well as a 1 sided A4 cover letter to Professor J. Helen Cross – ich.director@ucl.ac.uk

Closing date for applications: 29th April 2026.

Interview date: 11th May 2026

Applications that are submitted without following the correct application process will not be considered.

The successful applicant will then be required to apply to and register on the Child Health research degree to take up the PhD Studentship.

PhD start date: from 1st July 2026 onwards, subject to successful graduate application following interview.

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