Post-doc in Machine learning
CDD · Postdoc · 14 mois Bac+8 / Doctorat, Grandes Écoles Neurospin · Gif-sur-Yvette (France)
Date de prise de poste : 1 janvier 2022
imaging-genetics data integration machine learning data science
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 and carry out fundamental and clinical neurosciences studies. The BrainOmics team (http://www.brainomics.org) works in imaging-genetics, at the crossroad where neuroinformatics, bioinformatics and machine learning meet and in collaboration with Gustave Roussy, ICM-La Pitié-Salpétrière and CentraleSupelec.
Machine learning in imaging-genetics on EU-AIMS
The post-doc researcher will design new machine learning models incorporating multi-modal MRI and multi-omic data for prediction and stratification purposes, especially in ill-posed problems (very large p, small n). Then, he/she will perform the imaging-genetics analyses of the EU-AIMS cohort (aims-2-trials.eu), dedicated to Autism Spectrum Disorder (ASD).
- Genotype microarray, brain anatomical and functional MRI data integration.
- Develop innovative and integrative machine learning prediction and stratification models.
- Applications in clinical neurosciences, ASD.
PhD in one of the following fields : Data Science, Machine Learning, Applied Statistics, Data integration, Neuro-Imaging, Genomics. Fluent in english.
- Excellent skills in statistics and applied mathematics.
- Programming : Python, R, Matlab.
- Curiosity, taste for a multi-disciplinary environment and innovation.
- Knowledge in biomedical image analysis and/or genetics and/or neuroscience is an asset.
Good team player, strong motivation, rigor, autonomy and resourcefulness.
Duration : 14 months, starting in January 2022.
Location : NeuroSpin-CEA, Plateau de Saclay, Gif-sur-Yvette.
- Mihailov et al. (2020). Cortical signatures in behaviorally clustered autistic traits subgroups: a population-based study. Translational Psychiatry.
- Guigui et al. (2019) Network Regularization in Imaging Genetics Improves Prediction Performances and Model Interpretability on Alzheimer's Disease. ISBI 2019 Venise, Italie.
- Marquand et al. (2016). Beyond lumping and splitting: a review of computational approaches for stratifying psychiatric disorders. Biol Psy: CNNI.
Procédure : Please email your CV + cover letter by October 31st, 2021, to firstname.lastname@example.org and email@example.com.
Date limite : 31 octobre 2021
Offre publiée le 11 octobre 2021, affichage jusqu'au 1 janvier 2022