Doctoral Position in Lower-Field Cardiac Magnetic Resonance Imaging 100%

ETH Zürich; Zürich; Vor Ort; CHF 100.000 - 140.000; Vollzeit

Zusammenfassung

ETH Zurich's CMR group at the Institute for Biomedical Engineering seeks a Master’s-level researcher to advance MR pulse sequence design, reconstruction, and data inference for time-resolved heart imaging.

You will perform simulations and volunteer-based experiments, contributing to digital twins with clinical partners to show diagnostic value of low-field deep CMR imaging. This role blends physics, engineering and computing in a collaborative, international team.

Qualifikationen

• Master of Science in Electrical Engineering, Biomedical Engineering, Computer Science or Physics with top grades.
• Strong interest in medical imaging physics and signal processing.
• Excellent programming skills in C, MATLAB/Python and ML frameworks (TensorFlow/PyTorch).

Aufgaben

• Design MR pulse sequences and image reconstruction methods.
• Perform numerical simulations and experimental measurements on volunteers.
• Collaborate with clinical partners to develop digital twins and demonstrate diagnostic value of low-field CMR imaging.

Kenntnisse

Programming

Imaging physics

Data processing

Ausbildung

Master of Science (Electrical/Biomedical/CS/Physics)

Tools

C

MATLAB

Python

TensorFlow

PyTorch

Jobbeschreibung

The CMR group at the Institute for Biomedical Engineering develops Magnetic Resonance (MR) technology and methods to assess the cardiovascular system. We devise the next generation of diagnostic tools for quantification of blood flow, organ perfusion, metabolism and function, tissue composition, microstructure and mechanics. The group exploits principles from physics, electrical engineering and computer science to design highly efficient and sensitive imaging and inference approaches to help guide diagnosis and treatment in cardiovascular patients.

Project background

The project is based on previous and ongoing work of the group demonstrating benefits of ramping down high-performance clinical MR systems to lower static magnetic field strengths. In conjunction with advanced MR pulse sequence design and image reconstruction approaches, excellent image quality is achievable. Moreover, given the lower static field, options to stacked and concurrent imaging of electric and magnetic sources in the heart become possible. To this end, data for deep inference and digital twinning of the heart is obtained.

Job description

• 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

Profile

• 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

We offer

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.

Working, teaching and research at ETH Zurich

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