PhD: Translational X-ray Imaging & ML for Cancer Histology

ETH Zürich; Zürich; Vor Ort; CHF 50.000 - 70.000; Vollzeit

Benefits dieser Stelle

On-the-job training

Professional development

On-site infrastructure

Work-life balance

Zusammenfassung

ETH Zürich invites a highly motivated Doctoral Student to join a translational X-ray imaging team, contributing to machine-learning-based image reconstruction for fast histopathological assessment of tumor biopsies. The project spans cantonal hospitals and clinical workflows, with opportunities to publish and present at international venues.

You will work on breast tissue imaging, data pipelines, and algorithm development in a multidisciplinary setting, with strong emphasis on collaboration and

Qualifikationen

• Master's degree in physics, engineering or computer science.
• Strong Python, CV and ML skills applied to imaging.
• Knowledge of X-ray physics is a plus.

Aufgaben

• 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.
• Compare with standard histology and assess algorithm performance.
• Present results at international conferences and internal workshops.
• Summarize results in scientific publications.
• Support the professorship in teaching activities.

Kenntnisse

Python

Computer vision

Machine learning

English communication

Ausbildung

Master's degree (physics/engineering/CS)

Tools

Python libraries

Jobbeschreibung

ETH Zürich invites a highly motivated Doctoral Student to join a translational X-ray imaging team, contributing to machine-learning-based image reconstruction for fast histopathological assessment of tumor biopsies. The project spans cantonal hospitals and clinical workflows, with opportunities to publish and present at international venues.

You will work on breast tissue imaging, data pipelines, and algorithm development in a multidisciplinary setting, with strong emphasis on collaboration and