Offre en lien avec l’Action/le Réseau : – — –/– — –
Laboratoire/Entreprise : LITIS – Rouen
Durée : 3 ans
Contact : benoit.gauzere@insa-rouen.fr
Date limite de publication : 2024-10-01
Contexte :
Unlocking the Mysteries of the Brain with Graph-Based Machine Learning
In the fascinating world of neuroscience, understanding the brain’s intricate structure is key to unlocking the secrets of psychiatric and neurological disorders. Imagine if we could map the brain’s folds and curves to reveal patterns that indicate health or disease. This is precisely what our cutting-edge PhD project aims to achieve, leveraging the power of graph-based machine learning (GML) and Graph Neural Networks (GNN).
Project Overview
Our research focuses on enhancing the representation and analysis of neuroimaging data, particularly from MRI scans, using innovative GML techniques. By developing advanced models, we aim to identify individual traits such as gender and pathology with. What’s more, we are embedding principles of fairness into our models to ensure they are robust against variations in data acquisition and the natural diversity of brain structures.
Sujet :
Key Research Questions
– Hierarchical Information Analysis in Brain Graphs: How can we design GML models that effectively capture and utilize hierarchical information in brain graphs for better analysis of cortical folding patterns?
– Robustness to MRI Variations: Can a GML model trained on data from one MRI acquisition center generalize well to data from other centers, demonstrating robustness and enhancing the reproducibility of neuroimaging studies?
– Local Variation and Cognitive Functions: How can GML approaches help us identify and analyze local variations in brain anatomy, and what can these variations tell us about cognitive functions and neurological conditions?
Methodology and Resources
Our PhD candidate will have access to premier datasets, including:
– Human Connectome Project: Featuring top-quality MRI data from 1200 individuals.
– UK Biobank: Offering multimodal MRI data from over 10,000 individuals.
Supervisors and Research Environment
The project will be hosted at INSA Rouen, within the LITIS laboratory, and will be co-supervised by experts in the field:
Benoit Gaüzère
Guillaume Auzias
Sylvain Takerkart
Paul Honeine
Our multidisciplinary team brings together expertise in machine learning, computational anatomy, and neuroscience. The candidate will benefit from collaborations with leading research teams across multiple institutions.
How to Apply
Ready to embark on this exciting journey? Send your resume, academic results, and links to code or scientific papers to the following contacts. Please include “[FAMOUS]” in the subject line of your email:
Benoit Gaüzère: benoit.gauzere@insa-rouen.fr
Paul Honeine: paul.honeine@univ-rouen.fr
Guillaume Auzias: guillaume.auzias@univ-amu.fr
Sylvain Takerkart: sylvain.takerkart@univ-amu.fr
full offer is available here.
Profil du candidat :
Candidate Profile
We are seeking a passionate and dedicated PhD candidate with:
A Master’s degree in data science, computer engineering, or a related field.
Proficiency in Python programming.
Strong knowledge or experience in machine learning and data science.
Experience with graph structures is a plus.
High motivation and an interest in neuroscience.
Excellent reading, writing, and communication skills in English.
Formation et compétences requises :
Candidate Profile
We are seeking a passionate and dedicated PhD candidate with:
A Master’s degree in data science, computer engineering, or a related field.
Proficiency in Python programming.
Strong knowledge or experience in machine learning and data science.
Experience with graph structures is a plus.
High motivation and an interest in neuroscience.
Excellent reading, writing, and communication skills in English.
Adresse d’emploi :
LITIS INSA Rouen Normandie
Document attaché : 202406051242_PhD_project_Gauzere_Auzias.pdf