Postdoc position to study diagnosis from multimodal fMRI data for amyotrophic lateral sclerosis

Description

The project aims to develop and test prediction tools for clinical diagnosis from neuroimaging data, with pipelines covering data preprocessing and machine-learning representational techniques. The focus is on amyotrophic lateral sclerosis (ALS), a rare neurodegenerative disease for which no efficient treatment exists. A particular goal is to test the possibility of stratifying ALS patients in subtypes with slow versus fast disease progression. It is based on a dataset of 30 ALS patients and matched controls, acquired on 7T and 3T magnetic resonance imaging (MRI) scanners, which is currently being extended. Specifically, this dataset involves advanced MRI techniques that are promising for neurodegenerative diseases: quantitative and sodium MRI modalities, in addition to usual structural and functional MRI. We thus plan to build a multimodal prediction pipeline such as to test the combination of distinct types of MRI data (graph/network fusion) for clinical prediction. It also includes an international collaboration and aims to compare ALS datasets acquired in different centers.

Training required

PhD required

Desired profile

We are looking for a candidate that can demonstrate sufficient experience at the PhD level and knowledge in data analysis for neuroscientific data for reaching the project objectives. We seek profiles with solid programming skills in Python (or an equivalent language) combined with a background in the field of neurology. Experience in clinical neuroimaging, markers for neuropathologies and/or computational neuroscience will be favored. Additionally, experience with working in a interdisciplinary team and interest for open science are desired. Women are strongly encouraged to apply. The candidates will also be evaluated on their motivation, with respect to all steps of the process from data acquisition to analysis and scientific communication.

Company

The position lies at the intersection between clinical research in a hospital and a neuroscience laboratory, aiming to bridge the different research cultures from both sides. We provide an interdisciplinary environment with a team of experts in the following fields:
– computational neuroscience with Matthieu Gilson (https://matthieugilson.eu) and Xenia Kobeleva (https://xenia-kobeleva.com/)
– MRI techniques with Wafaa Zaaraoui (https://crmbm.univ-amu.fr/contact/zaaraoui-wafaa)
– neurology with Aude-Marie Grapperon (https://loop.frontiersin.org/people/593696) and again Xenia Kobeleva