PhD Position in Physics-Informed Machine Learning: The Institute for Biomedical Engineering at ETH Zurich offers a unique PhD position focused on leveraging physics-informed machine learning to enhance cardiac magnetic resonance (CMR) imaging and inferencing. This position provides an opportunity to contribute to cutting-edge research in cardiovascular diagnostics.
PhD Position in Physics-Informed Machine Learning for Cardiac Magnetic Resonance
Designation
PhD Student in Physics-Informed Machine Learning for Cardiac Magnetic Resonance
Research Area
- Cardiac Magnetic Resonance (CMR)
- Machine Learning & Deep Learning
- Computational Imaging
- Signal Processing
Location
ETH Zurich, Zรผrich, Switzerland
Eligibility/Qualification
| Degree | Required Expertise |
|---|---|
| Master of Science | – Computer Science – Electrical Engineering – Biomedical Engineering – Physics – Applied Mathematics |
| Skills | – Advanced Signal Processing – Programming in Python, C/C++ – Experience with deep learning frameworks (PyTorch, TensorFlow, Keras) – Familiarity with supervised machine learning on image data – Experience with large datasets |
Description
The PhD project aims to develop innovative strategies for accelerating data acquisition, physics-informed image reconstruction, and quantitative inference of cardiovascular anatomy and hemodynamics. The research is set within a highly interdisciplinary team of engineers, physicists, clinicians, and data scientists, backed by state-of-the-art imaging infrastructure and clinical datasets.
How to Apply
Interested candidates should submit their application, including a cover letter, CV, and contact information for references, via the ETH Zurich careers portal. For specific application instructions, please check the official ETH Zurich website.
Last Date for Apply
Applications are open until filled, but early application is encouraged.
This scholarship provides a remarkable opportunity for students eager to make a significant impact in the field of cardiovascular medicine using advanced machine learning techniques.
