PhD in Sparse 3D Imaging for Virtual Histology

NZZone; Zürich; Hybrid; CHF 52.000 - 64.000; Vollzeit

Zusammenfassung

ETH Zürich seeks a highly motivated Doctoral Student to contribute to translational X-ray imaging and the development of machine-learning-based sparse 3D reconstruction modalities. You will engage in pilot studies at cantonal hospitals and help improve clinical workflows for cancer patients, collaborating with clinicians and researchers across disciplines.

The role involves data collection, algorithm development and evaluation, with opportunities to present at conferences and publish findings.

Qualifikationen

• Excellent qualifications with a university degree (Master or equivalent) in physics, engineering or computer science.
• Proficiency in computer vision and machine learning with Python.
• Knowledge of X-ray physics and laboratory experience in imaging would be a benefit.
• Highly self-motivated, curious and committed character.
• Well organized and able to work in a multidisciplinary team.
• Excellent communication skills in English (written and spoken).

Aufgaben

• Collection of grating interferometry X-ray phase contrast microCT data of breast tissue biopsies.
• Setup of 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.
• Comparison with standard histology and assessment of algorithm performance.
• Present results at international conferences as well as internal workshops.
• Summarize results in scientific publications.
• Support the professorship in teaching activities.

Kenntnisse

Python

Computer vision

Machine learning

English communication

X-ray physics

Imaging lab techniques

Ausbildung

Master's degree in physics, engineering or computer science

Jobbeschreibung

ETH Zürich seeks a highly motivated Doctoral Student to contribute to translational X-ray imaging and the development of machine-learning-based sparse 3D reconstruction modalities. You will engage in pilot studies at cantonal hospitals and help improve clinical workflows for cancer patients, collaborating with clinicians and researchers across disciplines.

The role involves data collection, algorithm development and evaluation, with opportunities to present at conferences and publish findings.