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We are seeking an outstanding postdoctoral research fellow with
experience in deep learning / machine learning to work with us at
Caen University, France during one year on a project investigating
the analysis of time series of data corresponding to SPD matrices.
The challenge here will be to define machine learning methods and
more specifically deep learning methods (either convolutional or
recursive) to analyze these data by using all the interesting
properties of this specific manifold.</p>
<p style="margin-bottom: 0cm; line-height: 100%"><br>
</p>
<p><strong><font style="font-size: 14pt" size="4">Context</font></strong></p>
<p>The postdoctoral position is funded for one year under the
research project PredictAlert supported by the Region Normandy
(France). The PredictAlert project gathers engineering schools and
universities around the design of a better understanding of the
brain
states during different states of wakefulness.</p>
<p><strong><font style="font-size: 14pt" size="4">Objectives and
challenges</font></strong></p>
<p>The project is based on data from a cohort being currently
acquired. It includes EEG and MRI acquisitions performed while
subjects are falling asleep for a nap. In both cases, an
acquisition
in a given time window, is characterized by a SPD matrix. Each
entry
of this matrix correspond either to a correlation between two
sensors
in the case of an EEG acquisition or a correlation between two
brain’s zones in the case of an IRM acquisition.<br>
In a first
step the candidate will have to work on EEG acquisitions in order
to
design a deep learning algorithm predicting quantified levels of
wakefulness along long EEG sequences. Convolutional [1, 3] or
recurrent [2, 4] networks on the SPD manifold will be both studded
and evaluated before a focus on the more promising approach.</p>
<p><br>
<br>
</p>
<p>While functional IRM sequences may also be characterized as time
series of SPD matrices, these sequences are based on data with a
much
better spatial resolution than EEGs. This come at the price of a
much
lower temporal resolution compared EEG acquisitions. The candidate
will have to adapt the work already done on EEG data to functional
IRM datum and to compare both results.</p>
<p><strong><font style="font-size: 14pt" size="4">Candidate profile</font></strong></p>
<p><br>
<br>
</p>
<ul>
<li>
<p style="margin-bottom: 0cm"> The candidate must have a recent
Ph.D. (within 5 years) in Computer science (or Applied
Mathematics) in the field of Machine Learning.</p>
</li>
<li>
<p style="margin-bottom: 0cm"> Knowledge and experience within
Deep Learning frameworks is highly recommended.</p>
</li>
<li>
<p style="margin-bottom: 0cm"> The candidate will perform
research and algorithmic developments and solid programming
skills are required.</p>
</li>
<li>
<p style="margin-bottom: 0cm"> Interpersonal skills and the
ability to work well individually or as a member of a project
team are recommended.</p>
</li>
<li>
<p>Good written and verbal communication skills are required,
the candidate has to be fluent in spoken French or English and
written English. Working language can be English or French.</p>
</li>
</ul>
<p><strong><font style="font-size: 14pt" size="4">Location</font></strong></p>
<p><br>
Caen, France in the GREYC UMR CNRS laboratory. Situated in
the Normandy region of France close to the sea and about 240km
west
of Paris the city still has many old quarters, a population of
around
120,000 the city area has roughly 250,000 inhabitants. <a
href="https://caen.maville.com/info/detail-galerie_-Caen-en-images-_344_GaleriePhoto.Htm">Some
photos</a></p>
<p><strong><font style="font-size: 14pt" size="4">Application</font></strong></p>
<p>Interested candidates should submit their application to</p>
<ul>
<li>
<p style="margin-bottom: 0cm"><a class="moz-txt-link-abbreviated" href="mailto:luc.brun@ensicaen.fr">luc.brun@ensicaen.fr</a> and</p>
</li>
<li>
<p><a class="moz-txt-link-abbreviated" href="mailto:olivier.etard@unicaen.fr">olivier.etard@unicaen.fr</a></p>
</li>
</ul>
<p>Please include in your application email one Curriculum Vitae,
one
statement of research letter explaining your interest and your
skills
for this position, and 2 reference letters (all in a single PDF
file). Applications will be admitted until the position is filled.</p>
<p><strong><font style="font-size: 14pt" size="4">Additional
information</font></strong></p>
<p><br>
<strong>Host institution:</strong> ENSICAEN, University of
Caen Normandy and CNRS, GREYC laboratory (UMR 6072)<br>
<strong>Gross
Salary: </strong>between 2339 and 3268 euros per month
according to
experience (charges included)<br>
<strong>Duration: </strong>12
months.<br>
<strong>Starting date:</strong> from January
2020<br>
<strong>Advantages:</strong> Possibility of French courses,
participation in transport costs,<br>
possibility of restoration on
site.</p>
<p><strong><font style="font-size: 14pt" size="4">References</font></strong></p>
<p><br>
[1] Rudrasis Chakraborty, Jose Bouza, Jonathan Manton, and
Baba C Vemuri. Manifoldnet: A deep neural network for
manifold-valued data with applications. IEEE Transactions on
Pattern Analysis and Machine Intelligence, pages 1–1,
2020.<br>
[2] Rudrasis Chakraborty, Chun-Hao Yang, Xingjian Zhen,
Monami Banerjee, Derek Archer, David Vaillancourt, Vikas Singh,
and Baba Vemuri. A statistical recurrent model on the manifold
of symmetric positive definite matrices. In S. Bengio, H.
Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R.
Garnett, editors, Advances in Neural Information Processing
Systems, volume 31. Curran Associates, Inc., 2018.<br>
[3] Xuan
Son Nguyen, Luc Brun, Olivier Lezoray, and Sébastien Bougleux.
A neural network based on SPD manifold learning for
skeleton-based hand gesture recognition. In IEEE Conference on
Computer Vision and Pattern Regognition (CVPR), Long Beach,
United States, 2019.<br>
[4] Xuan Son Nguyen, Luc Brun, Olivier
Lézoray, and Sébastien Bougleux. Learning Recurrent High-order
Statistics for Skeleton-based Hand Gesture Recognition. In
International Conference on Pattern Recognition (ICPR -IEEE),
Milan (virtual), Italy, 2021.</p>
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