PhD Studentship: Automatic Calibration of Electrooculography Data for Accurate Quantification of Eye-movements
Core
Develop neural networks (AI) for automatic and continuous calibration of Electrooculography (EOG) data to enable retrospective diagnosis of dizziness/vertigo in home settings.
Role type
PhD Researcher (Computer Vision / Machine Learning)
Builds
AI models for signal calibration and retrospective clinical diagnosis
Domain
Healthcare technology / Medical diagnostics / Signal Processing
Deliverable
production ML models
Required skills
Neural networks, deep learning, electrooculography (EOG) signal processing, computer vision, algorithm development
Preferred skills
experience with home-based sensor data, non-clinical environment adaptation
Technologies
Python, PyTorch/TensorFlow (implied by neural networks), EOG hardware interfaces
Responsibilities
Develop neural networks for automatic calibration of EOG signals, adapt algorithms for non-clinical home environments, contribute to teaching activities for BSc/MSc courses
Seniority
PhD Researcher (entry-level research)