PhD: Sparse 3D X-ray Histology for Virtual Biopsy

Master in Integrated Building Systems ETH Zürich; Zürich; Vor Ort; CHF 60.000 - 75.000; Vollzeit

Benefits dieser Stelle

On-the-job training

Development opportunities

Healthy work-life balance and on-site–

Zusammenfassung

ETH Zurich is offering a Doctoral Student position in translational X-ray imaging within the X-ray Imaging Professorship and PSI collaboration. The role focuses on developing machine-learning-based sparse 3D image reconstruction for fast histopathological assessment of tumor biopsies using X-ray phase-contrast radiography and tomography.

You will design data pipelines, implement reconstruction algorithms, compare with standard histology, and present findings at conferences while contributing to

Qualifikationen

• Master or equivalent degree in physics, engineering or computer science.
• Proficiency in computer vision and machine learning with Python.
• Knowledge of X-ray physics and laboratory 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 and internal workshops.
• Summarize results in scientific publications.
• Support the professorship in teaching activities.

Kenntnisse

Python

Computer vision

Machine learning

English communication

Ausbildung

Master or equivalent degree in physics, engineering or computer science

Jobbeschreibung

ETH Zurich is offering a Doctoral Student position in translational X-ray imaging within the X-ray Imaging Professorship and PSI collaboration. The role focuses on developing machine-learning-based sparse 3D image reconstruction for fast histopathological assessment of tumor biopsies using X-ray phase-contrast radiography and tomography.

You will design data pipelines, implement reconstruction algorithms, compare with standard histology, and present findings at conferences while contributing to