Prostate cancer treatment remains a challenge due to inter-patient variability in tumor biology and response to irradiation. The TwinCaRT project, funded by the PEPR Santé Numérique, aims to shift from "one-size-fits-all" radiotherapy to a personalized, in-silico-driven approach. By creating a Digital Twin (DT), we can simulate treatment outcomes before they occur, optimizing the therapeutic ratio for each individual.
PhD Position 1 will specifically address the following objectives : i) Mechanistic Modeling: Enhancing a 3D multiscale model to simulate tumor growth and the "5 R’s of Radiobiology" (Repair, Reoxygenation, etc.) at a cellular level. ii ) Virtual-Real Synchronization: Developing algorithms to identify patient-specific biological parameters (e.g., cell density, hypoxia) using longitudinal quantitative MRI (qMRI) data and iii) Clinical Integration: Utilizing real-time data from MR-Linac platforms to update the Digital Twin during the course of treatment.
PhD Position Biological Adaptation of Radiotherapy Treatments via Quantitative MRI on MR-Linac (TARGETMR) aims to shift from standard treatments to biologically adapted radiotherapy. The specific objectives of this PhD include:
- Sequence Development: Developing new quantitative MRI sequences to be acquired during treatment sessions.
- Biological Feature Extraction: Exploiting images to extract specific biological characteristics of the tumor.
- Digital Twin Integration: Integrating these biological data (such as hypoxia) into a multi-scale Digital Twin to adapt the treatment for each specific patient.
- In Silico Optimization: Using the Digital Twin to simulate treatment outcomes and generate spatial distributions of surviving cells to optimize the therapeutic ratio.
Required Profiles
- Education: Master’s degree (or equivalent) in Medical Physics, Biomedical Engineering, or Applied Mathematics.
- Technical Skills: * Knowledge of quantitative MRI and image processing.
- Interest in mechanistic modeling and in silico simulations.
- Familiarity with radiotherapy principles and medical imaging workflows.
- Soft Skills: Ability to work in a multidisciplinary environment (clinical and research).
Why Join TWINCART?
This thesis is part of the PEPR Santé Numérique (National Digital Health Program), a major French research initiative. You will work at the LTSI (Laboratory of Signal and Image Processing), a national leader in Digital Twin technology for cancer treatment.
- Unique Technology: Access to the MR-Linac Unity platform at the TherA-Innov platform (Centre Eugène Marquis), which is supported by the Brittany Region and the French State.
- High Impact: Your work will directly contribute to improving local tumor control (cure rates) and reducing side effects through increased personalization of cancer treatments.
Consortium: Collaboration with other partners within the national TwinCaRT project.
Location: CEM/LTSI (Laboratory of Signal and Image Processing), Inserm U1099, Rennes, France.
Duration: 36 Months.
Funding: Co-funded by the Brittany Region (ARED) and the PEPR Santé Numérique TwinCaRT project.
How to Apply: Interested candidates should contact the project lead:
Pr. Renaud de Crevoisier Email: r.de-crevoisier@rennes.unicancer.fr.
Mme Anaïs Barateau Email: a.barateau@rennes.unicancer.fr
Pr. Oscar Acosta Email: oscar.acosta@univ-rennes.fr
| Fichier attaché | Size |
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| PhD Position.pdf | 493.02 Ko |
| PhD PositionTARGETMR.pdf | 411.44 Ko |