Approche intégrée de la simulation Monte Carlo et de l'intelligence artificielle pour la prédiction de la réponse en radiothérapie interne vectorisée // An Integrated Monte Carlo and Artificial Intelligence Framework for Response Prediction in Targeted Ra

This PhD project aims to improve radionuclide therapy, which is currently limited by inaccurate dose measurement using SPECT imaging.
Firstly, the project proposes the use of Monte Carlo simulations to generate realistic SPECT images and enhance quantification methods. Secondly, the project aims to integrate diverse pre-treatment data (e.g. imaging, clinical and biological data) into a structured database, and to develop AI algorithms to build predictive models. The final goal is to identify reliable biomarkers that can predict treatment response more accurately, and to link patient characteristics to dose distribution and therapeutic outcomes.
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This PhD project aims to improve radionuclide therapy, which is currently limited by inaccurate dose measurement using SPECT imaging.
Firstly, the project proposes the use of Monte Carlo simulations to generate realistic SPECT images and enhance quantification methods. Secondly, the project aims to integrate diverse pre-treatment data (e.g. imaging, clinical and biological data) into a structured database, and to develop AI algorithms to build predictive models. The final goal is to identify reliable biomarkers that can predict treatment response more accurately, and to link patient characteristics to dose distribution and therapeutic outcomes.
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Début de la thèse : 01/10/2026

Financement d'un établissement public Français

Université Bourgogne Europe

37 SPIM - Sciences Physiques pour l'Ingénieur et Microtechniques

This work is intended to be transdisciplinary, involving clinicians, IT specialists, and medical physicists. Applicants must hold at least an upper second-class degree or equivalent qualification in a relevant subject, such as computer science, applied mathematics, biomedical engineering or medical physics. A Master's degree in a relevant discipline and additional research experience would be advantageous. Candidates should be fluent in English or French. Personal characteristics: - Curiosity, independence, initiative and scientific rigour; - Interpersonal skills and professional discretion (work in a hospital environment).
This work is intended to be transdisciplinary, involving clinicians, IT specialists, and medical physicists. Applicants must hold at least an upper second-class degree or equivalent qualification in a relevant subject, such as computer science, applied mathematics, biomedical engineering or medical physics. A Master's degree in a relevant discipline and additional research experience would be advantageous. Candidates should be fluent in English or French. Personal characteristics: - Curiosity, independence, initiative and scientific rigour; - Interpersonal skills and professional discretion (work in a hospital environment).