Publisher
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CNRS

Just Landed

Postdoc in Machine-Learning-Based Reconstruction of Dental Cone-Beam CT CNRS in France

Degree Level

Postdoc

Field of study

Computer Science

Funding

Available

Deadline

Nov 17, 2026

Country flag

Country

France

University

CNRS

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Keywords

Computer Science
Biomedical Engineering
Medical Imaging
Deep Learning
Mathematics
Dentistry
Medical Science
Ct Imaging
Inverse Problem
Physics
ML

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About this position

This postdoctoral project is part of the collaborative Cone-Beam AI initiative linking Acteon, CREATIS, and the Institut de Mathématiques de Bordeaux (IMB). The work is focused on improving the quality of dental cone-beam computed tomography (CBCT) for diagnosis while reducing the radiation dose delivered to patients.

The recruited postdoc will develop new, more effective tomographic reconstruction algorithms, with a strong emphasis on machine learning and methods for solving inverse problems. The research sits at the intersection of medical imaging, reconstruction theory, and data-driven algorithm design.

Research activities include reconstruction from truncated data so that images remain as quantitative as possible inside the scanner field of view and beyond it, motion correction by estimating patient or scanner motion without relying on a preliminary reconstruction, correction of scattered X-ray radiation through deep-learning-based projection correction, and reduction of metal artifacts that degrade image quality and can interfere with diagnosis.

The postdoc will join CREATIS, a multidisciplinary laboratory with broad expertise in medical imaging and a major role in health technologies, and will be integrated into the tomoradio team. The position is based mainly at the Centre Léon Bérard in Lyon, within a local group embedded in the cancer center environment.

Funding details

Available

How to apply

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