The Institut du Cerveau et de la Moelle épinière – ICM (Brain & Spine Institute) – is an international brain and spinal cord research center. ICM brings patients, doctors and researchers together with the aim of rapidly developing treatments for disorders of the nervous system.

The Centre for Neuroinformatics is a transverse structure in the Institute, gathering researchers, engineers, and IT people, united to promote excellence in data management, data analysis, and scientific computing accross the whole ICM.

ICM is ideally located in the centre of Paris, within the Hôpital de la Pitié Salpétrière. Every week, ICM is buzzing with formal and informal events related to the human brain, which you are encouraged to attend. Salsa and yoga lessons are also available, among the many other non-scientific activities available at ICM and the hospital.


Data Manager/REDCap Administrator
Centre for Neuroinformatics

The Data Manager /REDCap administrator will participate in scientific data management IT projects, with specific responsibility for clinical data management and will act as the REDCap reference for ICM teams

More information here.

Engineer / Software developer

GRC 21 Alzheimer Precision Medicine – AramisLab

Analysis of multimodal longitudinal neuroimaging data in Alzheimer’s disease

More information here.

PhD fellowship


Multimodal analysis of neuroimaging and transcriptomic data in genetic fronto-temporal dementia

More information here.


Additionnally, the Centre for Neuroinformatics acts a relay for the internship offers within ICM, for projects with a strong Data Science/Mathematical component. These are unique opportunities to work within one the 28 research teams.

Deep learning for automatic quality control
At CENIR, the ICM neuroimagery platform

Results in neuroimaging studies heavily depend on the quality of the acquired data. For instance, subject motion will bias the cortical thickness evaluation. quality control (QC) is thus a criticalstep but today it still mostly rely on visual inspection of the dat. We propose to work toward a full automated QC procedure of structural MRI acquisition, based on deep learning networks.

More information here.

Deep learning for automatic image segmentation
At CENIR, the ICM  neuroimagery platform

In brain imaging studies, there are still challenging segmentation tasks, especially for small brain structures visible on specific acquisitions, and for pathological areas (tumor, stroke, etc.). As the CENIR runs different clinical studies, it has accumulated a large number of manually segmented masks that will be useful for training a convolutional neural network.

More information here.

Cannaniboid receptors in cortical visual encoding
Bacci team (Cellular physiology of cortical microcircuits)

The eye captures images on its retina. But what we “see” is a result of how the brain receives and rebuilds the signals from the retina. In the 1960s to 70s, pioneers Hubel and Wiesel (in the 1960s) revealed the pattern of organization of brain cells that process vision.

More information here

Automated detection of maneuvers and posture in a genetic model organism
Wyart team

Discovery of new drugs and functions of genes involved in pain and neurological disease relies on efficient ways to screen effects on animal behavior that are relevant for humans. As the Zebrafish genome shares 70% homology with human genes and is well-suited to fast and high throughput investigation of genes and molecules involved in human diseases.

More information  here

Using digital brain models to compare multiple cohorts of patients developing or at risk of developing Alzheimer’s disease
Aramis Lab

The goal of this internship is to use the models developped with the Alzheimer’s Disease Neuroimaging Initiative (ADNI, US patients cohort) and compare and pool data from two other cohorts: the INSIGHT-preAD cohort from the Pitié-Salpêtrière hospital and the Pharma-Cog study which aims to replicate the ADNI cohort with European participants.

More information here.

Multiple Sclerosis: evaluate the impact of demyelination and remyelination with PET & MRI imaging data

Stankoff team (Brain repair in Multiple Sclerosis)

Multiple Sclerosis (MS) is the leading cause of non-traumatic disability in young adults. Using a deep learning approach, in collaboration with INRIA, the internship will focus on the evaluation of demyelination and remyelination of white matter lesions. The team has developped with INRIA an algorithm that uses multimodal MRI data to reproduce the myelin PET signal in lesions.
More information here

For other job offers at ICM, see the recruitment website (in english) and in french.