PhD Studentship: Multimodal Detection System for Neurodegenerative Decline
Core
Develop a low-cost, smartphone/tablet-based platform for early detection of neurodegenerative decline by monitoring everyday behaviours and subtle changes in attention, visuospatial ability, and motor control.
Role type
PhD researcher (multimodal machine learning and digital biomarkers)
Builds
A validated monitoring platform, high-quality datasets, and a pathway to clinical translation
Domain
Healthcare / Neurodegenerative diseases / Digital health
Deliverable
production ML models | product features
Required skills
multimodal deep learning, eye tracking, inertial sensing, game-based assessment design, longitudinal data analysis, Human-AI feedback integration
Preferred skills
privacy-preserving data capture, clinical translation, patient/public involvement
Technologies
smartphone/tablet sensors, calibration-free eye tracking, touchscreen interactions, multimodal datasets
Responsibilities
design ecologically valid tasks, develop multimodal machine-learning models, refine tasks through Human-AI feedback, collect longitudinal data to identify digital biomarkers
Seniority
PhD candidate (early career researcher)
