Lieu : Institut Pasteur, Paris · Contrat : Stage · Rémunération : A négocier
The Institut Pasteur is a private, non-profit foundation. Its mission is to help prevent and treat diseases, mainly those of infectious origin, through research, teaching, and public health initiatives.
Background: A large body of neuroimaging research has documented a variety of abnormalities related to complex neuropsychiatric disorders and psychological traits. Building on these results, the study of neuroimaging phenotypes and their associated genetic and environmental factors has become a central component of ongoing research, with a strong potential for improving our understanding of disease etiologies and identifying promising therapeutic targets. Genetic studies have arisen as an area of particular interest in this context. Family-based and genome-wide association studies (GWAS) have shown that both mental disorders and neuroanatomical phenotypes are highly heritable, and highlighted strong genetic correlations between neuroimaging phenotypes and mental health outcomes. However, our knowledge about the genetic variations influencing human brain structure and function remains limited. In particular, questions remain on how to best design robust, non-biased descriptors of brain MRI (magnetic resonance imaging) phenotypes to better understand the underlying biological pathways and support the development of biomarkers addressing the lack of gold standards in mental health diagnosis.
Objectives: The goal of the internship is to conduct pilot analyses to investigate the potential of machine learning approaches to infer latent neuroimaging phenotypes displaying maximum fit with the genetics of mental health outcomes. The project build on readily available data and approaches developed by others and within the team.
Missions: Understand the objectives, constraints and data used for the proposed topic. Assess the suitability and implement existing machine learning tools to achieve the objective, and summarize this work to the team.
Applicants should have advanced expertise in Statistics, Bioinformatics, Computer Science or other relevant disciplines with a strong quantitative background. Previous experience working with large scale data are welcome.
Interested applicants should send their curriculum vitae, a cover letter explaining their motivation, and contact information for two references to Dr. Hugues Aschard (hugues.aschard@pasteur.fr) and Léo Henches (leo.henches@pasteur.fr).