Centre CEA Saclay
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 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, ICM-La Pitié-Salpétrière, Mondor Biomedical Research Institute.
In the BrainOmics team at Neurospin, the post-doc researcher will work on the conception of machine learning models incorporating multi-modal MRI radiomic and multi-omic data. Moreover, he/she will take part to the analysis of patients cohorts in imaging-genetics, about neuro-oncology pathologies.
- Data extraction and qualitfication for each modality separately.
- Train and test machine learning prediction models for each modality.
- Radiogenomic data integration.
- Applications in neuro-oncology : Primary CNS Lymphoma, pediatric High Grade Glioma.
PhD in one of the following fields : Data Science, Machine Learning, Applied Statistics, Radiomics, Radiogenomics, Neuro-Imaging, Genomics. Fluent in english.
- Expertise in statistics and applied mathematics
- Programming : Python, R, Matlab
- Curiosity, taste for multi-disciplinary environment and for innovation.
- Good communication skills, good personal relationship skills.
- Knowledge in biomedical image analysis and/or genetics and/or oncology is an asset.
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 email@example.com and firstname.lastname@example.org
Aerts et al. (2014). Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nature Communications.
Kickingereder et al. (2016). Radiogenomics of Glioblastoma: Machine Learning–based Classification of Molecular Characteristics by Using Multiparametric and Multiregional MR Imaging Features. Radiology.