Hetero-dimensional cardiovascular digital twins for precision medicine

Employing personalised computational models of the cardiovascular system (CVS) in clinical applications represents an exciting challenge due to the complexities in modelling physiologically accurate blood flow and the advanced methods and extensive computational resources required for model calibration to limited clinical data.

This PhD project is part of an international collaboration between New Zealand and Europe to develop the first personalised human digital representation that can be used for improving patient care. We envisage the translation of this methodology to shift the medical paradigm to treat diseases based on patient-specific physiology.

This project aims to develop integrated computational and software tools to streamline the generation of efficient, purpose-specific CVS models of variable complexity, leveraging 0D-1D-3D coupled components (heart and vessels). These tools will be based on energy-preserving coupling techniques between multi-dimensional cardiovascular components, and efficient algorithms for parameter identification based on available clinical data. This methodology will allow to simplify or extend a CVS model by switching 0D/1D/3D components on demand in different CVS portions and by reducing anatomical regions that can’t be calibrated due to unavailable clinical measurements. This framework will enable us to generate adaptive CVS models of the desired complexity to provide the efficiency and identifiability required for specific clinical applications and predict subject-specific cardiovascular dynamics.

• A degree in Computer Science,Engineering, Mathematics, Physics or similar areas of knowledge
• Python or C++ programming skills
  

HORIZON Action Grant Budget-Based: VITAL

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