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Our research program seeks to shift cardiac healthcare from reactive to proactive care by harnessing Artificial Intelligence technologies to enable early disease detection and personalised therapy. We offer PhD projects in four strategic areas:

• Explainable ECG Analytics
  Develop interpretable deep-learning models that extract subtle biomarkers from routine ECGs for non-invasive, early detection of cardiac disease.
• AI-Enhanced Diagnostic Imaging
  Build computer-vision pipelines to automate and standardise measurement of ventricular volumes, ejection fraction, myocardial perfusion, and other key metrics across echo, CT, MRI, and other medical images.
• Cardiac Digital Twins
  Create computational replicas of individual hearts that integrate multimodal information to simulate disease progression and predict responses to interventions.
• Next-Generation Deterioration Prediction
  Design real-time, in-hospital monitoring systems powered by machine learning to anticipate clinical deterioration in cardiac patients, improving safety and guiding timely interventions.

Research Team

CardiacAI is a multidisciplinary team of data/computer scientists and cardiologists (). We have established the CardiacAI Data Repository that brings large amounts of Australian healthcare data together in a secure environment for the purpose of supporting and accelerating research and innovation in cardiac care.

About the Candidate

We are looking for an enthusiastic and outstanding candidate with a Bachelor's degree in science, engineering or a related field, solid analytical and technical skills, and a strong interest in Artificial Intelligence and Health. The ideal candidate should have the ability to collaborate and communicate effectively with data scientists, clinicians, policymakers, advocacy groups and IT professionals, and should have a good understanding of data privacy and ethics. The candidate must meet аIJʹÙÍø requirements for PhD entry.

Contact Information

For more information about this program of research please contact: Professor Blanca Gallego Luxan (b.gallego@unsw.edu.au)