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class="gmail-western" align="center"> <span
style="font-family:arial,helvetica,sans-serif"><font size="2"><b>Understanding
individual differences in neuroimaging</b><b> using
multi-view machine learning. Methods and applications.</b></font></span></p>
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<div id="gmail-NewsPostDetailSummary"> <span
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<p
style="margin-bottom:0.25cm;direction:ltr;color:rgb(0,0,10);line-height:120%;text-align:left">We
are seeking candidates for a two years postdoctoral, for
developping 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 (<a href="http://www.ilcb.fr">http://www.ilcb.fr</a>),
and will be awarded through a competitive selection
process. The laureate will work in both the Institut de
Neurosciences de la Timone (<a
href="http://www.int.univ-amu.fr/">http://www.int.univ-amu.fr/</a>)
and the Laboratoire d'Informatique et Systèmes (<a
href="http://www.lis-lab.fr/">http://www.lis-lab.fr/</a>).<br>
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<p
style="margin-bottom:0.25cm;direction:ltr;color:rgb(0,0,10);line-height:120%;text-align:left"><span
style="font-family:arial,helvetica,sans-serif"><font size="2">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.</font></span></p>
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<p
style="margin-bottom:0.25cm;direction:ltr;color:rgb(0,0,10);line-height:120%;text-align:left"><span
style="font-family:arial,helvetica,sans-serif"><font size="2">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.</font></span></p>
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<p
style="margin-bottom:0.25cm;direction:ltr;color:rgb(0,0,10);line-height:120%;text-align:left"><span
style="font-family:arial,helvetica,sans-serif"><font size="2">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
imperatively contact <a
href="mailto:sylvain.takerkart@univ-amu.fr">sylvain.takerkart@univ-amu.fr</a>, <a
href="mailto:francois-xavier.dupe@lis-lab.fr">francois-xavier.dupe@lis-lab.fr</a> and
<a href="mailto:hachem.kadri@lis-lab.fr">hachem.kadri@lis-lab.fr</a>
before May 25 2018 for a first contact.<br>
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