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ENSAM

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Postdoctoral Researcher in AI Algorithm Development for Patient-Specific Knee Digital Twins Ecole Nationale Supérieure d'Arts et Métiers (ENSAM) in France

Degree Level

Postdoc

Field of study

Computer Science

Funding

Available

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Country

France

University

ENSAM

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Keywords

Computer Science
Biomedical Engineering
Mechanical Engineering
Materials Science
Medical Imaging
Artificial Intelligence
Computational Mechanics
Medical Science
Digital Twin Technology
Physics
Finite Element Analysis
ML

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About this position

This postdoctoral position is part of the ANR AIKNEE project, which aims to advance patient-specific digital twins for personalized medicine and surgical planning. The research focuses on improving the state of the art in knee digital twins by using state-of-the-art artificial intelligence methods to predict the mechanical response of knee ligaments, especially collateral and cruciate ligaments, with high confidence.

The work is designed to support decision-making for several patient groups, including people undergoing total knee arthroplasty, athletes or patients at risk of ligament injury, and studies of the impact of losing cruciate or collateral ligaments. The project brings together the TIMC laboratory in Grenoble, the I2M and LaBRI laboratories in Bordeaux, and Twinsight, a company specialized in knee digital twins for surgical planning.

The postdoctoral researcher will lead WP3, Reconstruction of mechanical fields. The main goal is to build a reduced-order model capable of computing full mechanical fields, such as stresses and strains, within seconds on the real, personalized geometry of a patient’s cruciate and collateral ligaments from joint kinematics.

The research combines several strands of computational and data-driven modelling. It involves processing and matching 3D geometric objects such as point clouds, surface and volumetric meshes from medical imaging, 3D scanning and photogrammetry, and combining these with motion-capture data to define kinematics and boundary conditions. It also includes adapting a physics-informed graph neural network of the MeshGraphNet type to the 3D ligament mesh, using forces and displacements, including at the insertions, to reconstruct 3D mechanical fields.

In addition, the project includes coupling this approach with an AI-augmented beam model whose personalised geometric parameters are encoded by a graph neural network. The model will be trained on a database of nonlinear finite element simulations of ligaments with hyperelastic behaviour, using patient-specific meshes produced by the project’s segmentation and meshing pipeline and anatomically relevant boundary conditions.

The opening is hosted at ENSAM on the Bordeaux campus within a collaborative research environment focused on digital twins, biomechanics, and AI-driven computational modelling.

Funding details

Available

How to apply

Apply through the official job advert on the French public service portal. Use the application link provided in the posting and follow the instructions on the portal.

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