Doctoral Position in Sparse Reconstruction of Virtual Histology Images
Develop machine-learning-based 3D image reconstruction for advanced oncological virtual histology.
Best for:Best for computer vision and machine learning researchers with a background in physics or engineering interested in translational medical imaging.
Overview
The X-ray Imaging Professorship at ETH Zurich and the Paul Scherrer Institute is seeking a motivated doctoral student for translational X-ray imaging research. The project focuses on designing and implementing machine-learning-based sparse 3D image reconstruction methods to assess tumor biopsies rapidly using X-ray phase-contrast radiography and tomography. The successful candidate will collaborate within a multidisciplinary team of academic and clinical researchers to enhance clinical workflows and support teaching activities. Applicants are expected to hold a relevant master's degree and bring strong programming and machine learning skills.
What you'll do
- 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 reconstructions with standard histology and assess algorithm performance
- Present research findings at international conferences and internal workshops
- Summarize results in scientific publications
- Support the professorship in teaching activities
Requirements
Degree required: Master or equivalent in physics, engineering, or computer science
Degree offered: PhD
Language: English; excellent written and spoken proficiency
Instruction: English
- Proficiency in computer vision and machine learning using Python
- Strong background in physics, engineering, or computer science
- Willingness to conduct research within a multidisciplinary team
- Excellent organizational skills and self-motivation
- Strong written and verbal communication skills in English
Funding & terms
How to apply
Submit your application exclusively through the online application portal. Applications sent via email or postal services will not be considered.
- Motivation letter
- CV
- Publication List
- Transcripts
- 2 reference letters
Contact
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