[Comp-neuro] Postdoc position in Marseille, France: machine learning and neuroscience

Sylvain Takerkart Sylvain.Takerkart at univ-amu.fr
Wed May 9 16:19:12 CEST 2018


*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 at univ-amu.fr,
francois-xavier.dupe at lis-lab.fr and hachem.kadri at 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
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