Deep learning for cardiac medical image analysis

We are seeking a highly motivated PhD student to join an exciting interdisciplinary project focused on artificial intelligence and deep learning for cardiac medical image analysis. This project investigates how advanced computational methods can be used to automatically analyze cardiac CT and MRI, with the goal of improving disease characterization, risk stratification, and clinical decision-making in cardiovascular disease.

The project focuses on the development and application of state-of-the-art deep learning and computer vision techniques for tasks such as cardiac structure segmentation, tissue characterization, disease phenotype modeling, and spatial biomarker extraction. By leveraging large-scale cardiac imaging datasets, the successful candidate will build AI models capable of learning clinically meaningful patterns from complex 3D medical images.

The student will gain hands-on experience with medical image preprocessing, neural network architecture design, model training and evaluation, and multimodal data integration. The project also emphasizes interpretable AI, robust validation, and translation of algorithmic outputs into clinically relevant insights.

This PhD is inherently cross-disciplinary and will be co-supervised by experts in medical imaging, artificial intelligence, and cardiovascular medicine. It is well suited for candidates with a background in computer science, engineering, data science, or applied mathematics who are interested in medical applications.

Desired skills

• A background in computer science, engineering, mathematics, data science, biomedical engineering, or related fields
• Interest or experience in deep learning, machine learning, or computer vision
• Ability to program in Python and work with deep learning frameworks such as PyTorch is preferred
• Experience with medical imaging (CT/MRI) is an advantage but not required
• Strong motivation to work on AI for healthcare in a collaborative research environment