Machine learning in imaging-genetics on UK Biobank

Type de poste
Niveau d'étude minimal
Durée du poste
Contrat renouvelable
Contrat non renouvelable
Date de prise de fonction
Date de fin de validité de l'annonce
Nom de la structure d'accueil

Centre CEA Saclay
RN 306
91191 Gif-sur-Yvette

Cathy Philippe
Vincent Frouin
Email du/des contacts

NeuroSpin is an outstanding research center on the Human brain. Part of the CEA (Atomic Energy Commission) and Paris-Saclay University, the NeuroSpin teams are leaders in very high field MRI carry out studies in fundamental and clinical neurosciences. The BrainOmics team works in imaging-genetics, at the crossroad where neuroinformatics, bioinformatics and machine learning meet and in collaboration with Gustave Roussy and ICM-La Pitié-Salpétrière.

In the BrainOmics team at Neurospin, the post-doc researcher will work on the conception of machine learning models incorporating multi-modal MRI and multi-omic data. Moreover, he/she will take part to the analysis in imaging-genetics of UK Biobank cohort (genetics for 500K, MRI for 25K, WES for 50K subjects)

Post-doc Activities

  • Data quality control and inspection for each modality separately.
  • Train machine learning prediction models for each modality.
  • Genotype microarray, WES, MRI data integration.
  • Applications in neurosciences.

Searched profile
PhD in one of the following fields : Data Science, Machine Learning, Applied Statistics, Data integration, Neuro-Imaging, Genomics. Fluent in english.

Job-related skills

  • Very good skills in statistics and applied mathematics
  • Programming : Python, R, Matlab
  • Curiosity, taste for multi-disciplinary environment and for innovation.
  • Knowledge in biomedical image analysis and/or genetics and/or neruoscience is an asset.

Behavioral skills
Good team player, strong motivation, rigor, autonomy and resourcefulness.

Duration : 2 years, starting in September 2019.
Location : NeuroSpin-CEA, Plateau de Saclay, Gif-sur-Yvette.
Please email your CV + cover letter by May 30th, 2019 to and

Castel et al. (2018) Modified penetrance of coding variants by cis-regulatory variation shapes human traits. Nature Genetics.
Guigui et al. (2019) Network Regularization in Imaging Genetics Improves Prediction Performances and Model Interpretability on Alzheimer's Disease. ISBI 2019 Venise, Italie.
Tenenhaus et al. (2014). Variable selection for generalized canonical correlation analysis. Biostatistics.

Equipe adhérente personne morale SFBI
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