PhD in Sparse 3D X-ray Imaging for Virtual Histology

1000scholars; Zürich; On-site; CHF 60,000 - 90,000; Full time

Benefits offered by this job

On-site infrastructure

On-the-job training

Professional development opportunities

Job summary

ETH Zurich is seeking a highly motivated Doctoral Student to join a translational X-ray imaging team, contributing to sparse 3D reconstruction for fast histopathology using X-ray phase-contrast radiography and tomography. The role involves multidisciplinary collaboration with clinical partners and ongoing pilot studies in hospitals.

You will design and implement ML-based reconstruction methods, analyze data from breast tissue biopsies, publish findings, and support teaching activities within the

Qualifications

• Master's or equivalent in physics, engineering or computer science.
• Proficiency in computer vision and machine learning with Python.
• Knowledge of X-ray physics and imaging would be a benefit.

Responsibilities

• Collect grating interferometry X-ray phase-contrast microCT data of breast tissue biopsies.
• Set up data pipelines for synchrotron and laboratory microCT scans of soft tissue.
• Develop algorithms for reconstruction of virtual histopathology images from sparse phase-contrast CT acquisitions of oncological specimens.
• Compare with standard histology and assess algorithm performance.
• Present results at international conferences as well as internal workshops.
• Summarize results in scientific publications.
• Support the professorship in teaching activities.

Skills

Computer vision

Machine learning

Python

Education

Master or equivalent (physics, engineering or computer science)

Job description

ETH Zurich is seeking a highly motivated Doctoral Student to join a translational X-ray imaging team, contributing to sparse 3D reconstruction for fast histopathology using X-ray phase-contrast radiography and tomography. The role involves multidisciplinary collaboration with clinical partners and ongoing pilot studies in hospitals.

You will design and implement ML-based reconstruction methods, analyze data from breast tissue biopsies, publish findings, and support teaching activities within the