Doctoral Position in Lower-Field Cardiac Magnetic Resonance Imaging

The position concerns MR pulse sequence design and image reconstruction, data inference and fusion using classical and physics-informed machine learning approaches to advance time-resolved morphological and tissue property imaging of the heart. The work will involve numerical simulations, experimental measurements and applications on volunteers. Together with the team and clinical partners, example data in patients will be obtained to populate digital twins and to indicate the diagnostic value of lower-field deep CMR imaging.

We are a dynamic and international team embedded in both electrical engineering and the medical faculty, with a long-standing track record in CMR research. First-class infrastructure is available, including a full range of experimental and clinical MR systems fully dedicated to research, state-of-the-art local and scalable cloud-based compute infrastructure (CPU, GPU) and workshops for mechanical, electrical and electronic development projects. Long-standing and very successful cooperations with industry and clinical partners (cardiology, radiology) offer opportunities for networking as well as for deployment of research results in real-world applications.

• You hold a Master of Science degree with first-class grades in: Electrical engineering, Biomedical engineering, Computer science or Physics

• You have developed a keen interest in medical imaging physics, signal and data processing

• Very good programming skills (C, Matlab/Python, TensorFlow/PyTorch) and a passion for both theoretical and practical work are a premise

• The ability to develop own lines of thought, to critically interpret data and a drive to innovate are further requirements along with curiosity, open mindedness, team player and communication skills