Implementing AI‑Enabled Wearable Technologies for Symptom Prediction in Parkinson’s Disease: A PD‑LIFE Project

About the Project

OPEN FOR UK AND REPUBLIC OF IRELAND STUDENTS ONLY

Applications are invited by Ulster University for a PhD in Parkinson’s disease research as part of a cohort of nine linked PhD studentship positions across Ireland in the exciting PD-LIFE project.

PD-LIFE is an all-island emerging hub of excellence that unites researchers, clinicians, people with Parkinson’s, and advocacy partners to transform the understanding and care of Parkinson’s disease (PD) across Ireland.

This full-time 3 year PhD studentship focuses on the use of technology to assess symptoms of PD and for PD prediction. The key aim of this PhD is to develop and validate AI systems to predict, classify, and personalise symptom monitoring and support.

Responsibilities will include:

• Assessing the accuracy and reliability of relevant off-the-shelf wearable sensors and their suitability to measure PD symptoms.
• Collect, process, and integrate multimodal data (movement, heart rate, galvanic skin response, activity logs) from wearable sensors used by participants in exercise and dance interventions.
• Build and evaluate AI / machine learning models using labelled, collected multimodal data to classify motor and non-motor symptoms, identify digital biomarkers, evaluate and predict symptom progression.
• Develop AI-informed systems that deliver personalised prompts or feedback to support motivation and adherence to physical activity.
• Conduct participatory evaluation of usability and acceptability with people with PD, caregivers, and clinicians.

Skills required of the Applicant:

• A minimum qualification of a 2.1 honours BSc or MSc in Computer Science / Artificial Intelligence or related discipline.
• Experience of working with sensors.
• Strong interest in carrying out innovative research in a health context.
• The successful candidate will be highly analytical and motivated with good interpersonal and organisational skills and will be self-managed and achievement oriented.