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TrueBeat: Interpretable and Trustworthy AI for Early Arterial Fibrillation Diagnosis

South East Technological University (Waterford) Research Support Unit
✓ Fully Funded ⏰ Closing Soon 🎓 Biomedical Engineering 🎓 Cardiology 🎓 Computer Science 🎓 Electrical Engineering 🎓 Electronic Engineering 🎓 Health Informatics 🎓 Machine Learning machine learning AI healthcare signal processing ECG atrial fibrillation wearable technology

Fully funded PhD developing interpretable AI models for early detection of atrial fibrillation using ECG data.

Project Description

This PhD project focuses on developing interpretable, robust, and energy-efficient AI models for early detection of atrial fibrillation (AF) using ECG data. The research will involve signal processing for ECG denoising, development of advanced AI models (including CNNs, LSTMs, and Transformers), and implementation of explainable AI techniques to improve clinical interpretability. The project will also address model generalisation across datasets and wearable devices, and explore green AI approaches for computational efficiency. The candidate will contribute to building a real-time AF detection prototype system, supporting improved early diagnosis and clinical decision-making.

Entry Requirements

Bachelor’s and postgraduate degree in relevant field (e.g. Biomedical Engineering, Computer Science)
• Experience in Python/MATLAB, machine learning, or signal processing
• Strong analytical and programming skills
• Interest in AI for healthcare and wearable technologies
• English language proficiency (if required)

How to Apply

Apply via SETU online application portal with all required documents.

Eligibility

UK/Home
EU
International

Supervisor Profile

DB
Dr Bhaskar Murari; Dr Arun Sankar; Dr Ondrej Kucera
South East Technological University (Waterford), Research Support Unit

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