PhD Studentship: Causal AI and EEG Analysis to Characterise Sleep in Children with Neurodevelopmental Conditions
Core
Develop algorithms to extract meaningful information from paediatric EEG recordings and apply causal AI to separate genuine sleep-related causes and effects from confounding factors for children with neurodevelopmental conditions.
Role type
PhD researcher in causal AI and EEG signal processing
Builds
Computational analysis tools for paediatric EEG and causal inference models
Domain
Paediatric neuroscience, clinical AI, signal processing
Deliverable
research
Required skills
signal processing, deep learning, artificial intelligence (AI), algorithm development, causal inference
Preferred skills
experience processing brain activity
Technologies
EEG recordings, deep learning frameworks
Responsibilities
Develop algorithms to extract features capturing sleep organisation, variability, and atypical patterns from paediatric EEG; examine causal relationships between sleep and neurodevelopmental conditions; ensure algorithms are interpretable, robust, and fair.
Seniority
PhD candidate