*Understanding individual differences in neuroimaging** using multi-view machine learning. Methods and applications.* * We are seeking candidates for a two years postdoctoral, for developing new machine learning methods to deal with heterogeneous data such as anatomical, functional and diffusion MRI. This post-doc will be funded by the newly established Institute for Language, Communication and the Brain in Marseille, France (http://www.ilcb.fr <http://www.ilcb.fr>), and will be awarded through a competitive selection process. The laureate will work in both the Institut de Neurosciences de la Timone (http://www.int.univ-amu.fr/ <http://www.int.univ-amu.fr/>) and the Laboratoire d'Informatique et Systèmes (http://www.lis-lab.fr/ <http://www.lis-lab.fr/>). * In brain imaging, traditional group analyses rely on averaging data collected in different individuals. This averaging offers a summary representation of the studied group, thus providing a way to perform inference at the population level. However, it discards the specificities of each individual, which have recently proved to carry critical information to develop diagnosis and prognosis tools for neurological and psychiatric diseases or to understand high level cognitive processes. Estimating robust population-wise invariants while preserving individual specificities is a challenge that can be addressed by integrating the information offered by different neuroimaging modalities, such as anatomical, functional and diffusion MRI, which respectively allow assessing brain shape, activity and connectivity. This can therefore be framed as a multi-view machine learning question. The tasks of the post-doctoral fellow will consist in 1. finding adequate representations of data (e.g. graph, stack of images, …) that preserve structural information, 2. designing and implementing machine learning algorithms that exploit both the representations and the multiple views using kernel methods and/or neural networks, and 3. evaluating them on a variety of MRI datasets dedicated to studying language and communication. The candidate should have completed a PhD in computer science, applied mathematics or electrical engineering, with a focus on machine learning. He/she should also have a strong motivation to work in neuroscience, as the working environment will be truly inter-disciplinary. Interested candidates should contact sylvain.takerkart@univ-amu.fr, francois-xavier.dupe@lis-lab.fr and hachem.kadri@lis-lab.fr before May 25 2018 for a first contact. -- Sylvain Takerkart Institut des Neurosciences de la Timone (INT) UMR 7289 CNRS-AMU Marseille, France tél: +33 (0)4 91 324 007 http://www.int.univ-amu.fr/_TAKERKART-Sylvain_?lang=en