developing advanced signal analysis and artificial intelligence for the identification of childhood epilepsy in community settings.

Sleep difficulties are common in children with neurodevelopmental conditions and can have profound effects on cognition, behaviour, emotional wellbeing, learning and family life. Yet, measuring sleep objectively remains difficult, and much remains unknown about what causes and effects link sleep and neurodevelopmental conditions. To shed light on this important clinical question, this PhD project will develop and apply new computational analysis of EEG recordings to achieve a richer understanding of sleep in children with neurodevelopmental conditions. You will develop algorithms to extract meaningful information from paediatric EEG recordings, including features that capture sleep organisation, variability, and atypical patterns. You will also examine how causal AI can be used to separate genuine sleep-related causes and effects from confounding factors. Our aim is not only to create algorithms that are accurate in detecting abnormalities in sleep but that are also interpretable, robust, and fair.

You must have a strong technical background in signal processing and/or AI, be proactive and eager to work in a highly interdisciplinary environment at the frontier of AI for healthcare, paediatric neuroscience, and clinically relevant signal processing. This is an exciting opportunity to contribute to research with strong potential for future real-world impact.

Early application is strongly encouraged – the PhD studentship will be awarded once a suitable candidate is found. Start date is flexible.

developing advanced signal analysis and artificial intelligence for the identification of childhood epilepsy in community settings.

Sleep difficulties are common in children with neurodevelopmental conditions and can have profound effects on cognition, behaviour, emotional wellbeing, learning and family life. Yet, measuring sleep objectively remains difficult, and much remains unknown about what causes and effects link sleep and neurodevelopmental conditions. To shed light on this important clinical question, this PhD project will develop and apply new computational analysis of EEG recordings to achieve a richer understanding of sleep in children with neurodevelopmental conditions. You will develop algorithms to extract meaningful information from paediatric EEG recordings, including features that capture sleep organisation, variability, and atypical patterns. You will also examine how causal AI can be used to separate genuine sleep-related causes and effects from confounding factors. Our aim is not only to create algorithms that are accurate in detecting abnormalities in sleep but that are also interpretable, robust, and fair.

You must have a strong technical background in signal processing and/or AI, be proactive and eager to work in a highly interdisciplinary environment at the frontier of AI for healthcare, paediatric neuroscience, and clinically relevant signal processing. This is an exciting opportunity to contribute to research with strong potential for future real-world impact.

Early application is strongly encouraged – the PhD studentship will be awarded once a suitable candidate is found. Start date is flexible.