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<p>The FOX team from the CRIStAL laboratory (UMR CNRS), Lille France
is looking to recruit a PhD student <b>starting as soon as
possible </b>on the following subject : Spiking Neural Networks
for Video Analysis<br>
</p>
The FOX research group is part of the CRIStAL laboratory (University
of Lille, CNRS), located in Lille, France. We focus on video
analysis for human behavior understanding. Specifically, we develop
spatio-temporal models of motions for tasks such as abnormal event
detection, emotion recognition, and face alignment. We are also
involved in IRCICA (CNRS), a research institute promoting
multidisciplanary research. At IRCICA, we collaborate with computer
scientists and experts in electronics engineering to create new
models of neural networks that can be implemented on low-power
hardware architectures. Recently, we designed state-of-the-art
models for image recognition with single and multi-layer
unsupervised spiking neural networks. We were among the first to
succesfully apply unsupervised SNNs on modern datasets of computer
vision. We also developed our own SNN simulator to support
experiments with SNN on computer vision problems.<br>
Our work is published in major journals (Pattern Recognition, IEEE
Trans. on Affective Computing) and conferences (WACV, IJCNN) in the
field.<br>
<br>
<b>Abstract</b>: Spiking Neural Network have recently been evaluated
on classical image recognition tasks. This work has highlighted
their promising performances in this domain and have identified ways
to improve them to be competitive with comparable deep learning
approaches. In particular, it demonstrated the ability of SNN
architectures to learn relevant patterns for static pattern
recognition in an unsupervised manner. However, dealing with static
images is not enough, and the computer vision community is
increasingly interested in video analysis, for two reasons. First,
video data is more and more common and corresponds to a wide range
of applications (video surveillance, audio-visual productions,
autonomous vehicles...). Second, this data is richer than isolated
static images, and thus offers the possibility to develop more
effective systems, e.g. using motion information. Thus, it is
recognized in the community that modeling motion in videos is more
relevant than studying visual appearance alone for tasks such as
action or emotion recognition. The next step for SNNs is therefore
to study their ability to model motion rather than, or in addition
to, image appearance.<br>
<br>
The goal of the Ph.D. candidate will be to explore the use of SNNs
for space-time modeling in videos. This work will be targeted
towards applications in human behavior understanding and especially
action recognition. More specifically, the Ph.D. candidate is
expected to:<br>
* identify what issues may prevent space-time modeling with SNNs
and how they can be circumvented;<br>
* propose new supervised and unsupervised SNN models for motion
modeling, which are compatible with hardware implementations on
ultra-low power devices;<br>
* evaluate the proposed models on standard datasets for video
analysis.<br>
<p>Detailed subject: <a class="moz-txt-link-freetext"
href="https://bit.ly/stssnnfox">https://bit.ly/stssnnfox</a><br>
</p>
Candidates must hold a Master degree (or an equivalent degree) in
Computer Science, Statistics, Applied Mathematics or a related
field. Experience in one or more of the following is a plus:<br>
• image processing, computer vision;<br>
• machine learning;<br>
• bio-inspired computing;<br>
• research methodology (literature review, experimentation…).<br>
<br>
Candidates should have the following skills:<br>
• good proficiency in English, both spoken and written;<br>
• scientific writing;<br>
• programming (experience in C++ is a plus, but not
mandatory).<br>
<br>
This PHD thesis will be funded in the framework of the ANVI-Luxant
industrial chair. The general objective of the Chair is to make a
scientific and technological progress in the mastery of emerging
information processing architectures such as neuromorphic
architectures as an embedded artificial intelligence technique. The
use-case studies will come from video protection in the context of
retail and transportation.<br>
<br>
The candidate will be funded for 3 years; he/she is expected to
defend his/her thesis and graduate by the end of the contract. The
monthly gross salary is around 2000€, including benefits (health
insurance, retirement fund, and paid vacations).<br>
<br>
The position is located in Lille, France. With over 110 000
students, the metropolitan area of Lille is one France's top
education student cities. The European Doctoral College Lille
Nord-Pas de Calais is headquartered in Lille Metropole and includes
3,000 PhD Doctorate students supported by university research
laboratories. Lille has a convenient location in the European
high-speed rail network. It lies on the Eurostar line to London
(1:20 hour journey). The French TGV network also puts it only 1 hour
from Paris, 35 mn from Brussels, and a short trips to other major
centres in France such as Paris, Marseille and Lyon.<br>
<br>
For application, please send the following information in a single
PDF file to Dr. Marius Bilasco (<a class="moz-txt-link-abbreviated
moz-txt-link-freetext" href="mailto:marius.bilasco@univ-lille.fr">marius.bilasco@univ-lille.fr</a>)
with subject [PhD_Luxant-ANVI]:<br>
* A cover letter.<br>
* A curriculum vitae, including a list of publications, if any.<br>
* Transcripts of grades of Master's degree.<br>
* The contact information of two references (and any letters if
available).<br>
<br>
We look forward to receiving your application as soon as possible,
but no later than 4.4.2022
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