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# Job offer

- **Company:** CNRS
- **Location:** Mions, France
- **Contract:** Temporary contract
- **Skills:** C++ (Programming Language), Computer Programming, Image Quality, Python (Programming Language), Machine Learning, Deep Learning, Information Technology
- **Published:** October 27, 2026
- **Apply:** https://emploi.cnrs.fr/Offres/CDD/UMR5220-SIMRIT-010/Default.aspx

## About the Role

PhD or equivalent

Research Field
Computer science

Education Level
PhD or equivalent

Research Field
Mathematics, Research Field
Computer science

Years of Research Experience
1 - 4

Research Field
Mathematics

Years of Research Experience
1 - 4, Academic background: PhD in applied mathematics, computer science, or medical physics,
Scientific interests: applied mathematics, computer science (medical image processing), medical physics,
Programming skills: Python, C++ (ITK, RTK),
Languages: English required, French optional.

## Description

In collaboration with the other project members, the postdoctoral researcher's activities will focus on the following topics:

 - CBCT reconstruction from truncated data: the goal is to achieve quantitative reconstruction within the scanner's field of view, and the best possible reconstruction outside of it.
 - Motion correction: involuntary movements of the patient or the scanner during acquisition introduce artifacts that can be corrected using motion estimation. The objective here will be to estimate this motion without relying on a prior reconstruction, using mathematical equations known as data consistency conditions.
 - Scatter radiation: CBCT reconstruction assumes that scattered radiation is not measured, which is not the case in practice and significantly degrades CBCT images. The objective will be to correct X-ray projections by estimating this scattered radiation through a deep learning-based approach.
 - Metal artifacts: metals within the field of view cause significant image quality degradation that can compromise diagnosis; these could also be corrected in the projections using deep learning.