Doctoral Position in Lower-Field Cardiac Magnetic Resonance Imaging
Develop next-generation lower-field cardiovascular magnetic resonance imaging and machine learning tools at ETH Zurich.
Best for:Best for master's graduates in engineering or physics with strong programming skills and an interest in medical imaging and machine learning.
Overview
The Cardiac Magnetic Resonance (CMR) group at ETH Zurich's Institute for Biomedical Engineering invites applications for a doctoral position focused on lower-field cardiac magnetic resonance imaging. The project explores reducing clinical MR field strengths to improve cardiovascular diagnostics through advanced pulse sequence design, image reconstruction, and machine learning. The successful candidate will conduct numerical simulations, experimental measurements, and volunteer studies while collaborating closely with clinical partners. Applicants are expected to hold a Master of Science degree with top grades and possess strong programming and analytical skills.
What you'll do
- Design novel MR pulse sequences and image reconstruction algorithms for cardiovascular imaging
- Implement classical and physics-informed machine learning approaches for data inference and fusion
- Conduct numerical simulations, experimental measurements, and imaging applications on healthy volunteers
- Collaborate with clinical partners to acquire patient data and populate cardiac digital twins
- Analyze imaging data critically to evaluate the diagnostic value of lower-field cardiac magnetic resonance
Requirements
Degree required: Master of Science degree in Electrical Engineering, Biomedical Engineering, Computer Science, or Physics
Degree offered: PhD
Language: English; excellent communication skills are required
Instruction: English
- Strong background in medical imaging physics, signal processing, or data processing
- Proficiency in programming languages such as C, MATLAB, or Python
- Experience with machine learning frameworks like TensorFlow or PyTorch
- Demonstrated academic excellence with first-class grades in prior studies
- Ability to think independently, innovate, and interpret complex scientific data
- Must hold an eligible Master of Science degree prior to commencement
Funding & terms
How to apply
Candidates must submit their application exclusively through the online application portal. Emailed or postal applications will not be reviewed. Prepare all required documents into the specified submission format.
- Motivation letter
- Detailed CV
- Study subjects including grades
- Contact information of two referees
Contact
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