[visionlist] One year Post doc position in Caen (France) on time series analysis for the neural characterization of different levels of wakefulness.
luc.brun at e.email
Tue Nov 16 11:46:43 -04 2021
We are seeking an outstanding postdoctoral research fellow with
experience in deep learning / machine learning to work with us at Caen
on a project investigating the analysis of multi-modal time series for
the characterization of brain functional connectivity in different
levels of wakefulness.
Host institution: ENSICAEN, University of Caen Normandy and CNRS, GREYC
laboratory (UMR 6072)
Gross Salary: between 2339 and 3268 euros per month according to
experience (charges included)
Duration: 12 months.
Starting date: from January 2020
Advantages: Possibility of French courses, participation in transport
costs, possibility of restoration on site.
Brain activity can be recorded in humans either by techniques based on
the metabolic functioning of the neuron, such as Positron Emission
(PET) or Magnetic Resonance Imaging (MRI), or by techniques based on the
electrical functioning of the neuron, such as electroencephalography
magnetoencephalography (MEG). If the first type of measurement allows to
obtain recordings with an interesting spatial resolution, the second
a higher temporal resolution. To summarize, these two approaches are
both imperfect but complementary.
In this project, we are interested by this double approach in the
context of the human characterization of different levels of wakefulness
(from full awake
to deep sleep). Data in animals indicate that the transition from
wakefulness to sleep causes changes in the relationships between
different brain structures.
Sensory inputs via the thalamus are inhibited leading to a decrease in
thalamo-cortical links in favor of intra-cortical relations. These
sively isolate the cortex in order to facilitate the descent into sleep.
In terms of connectivity, these modifications are reflected in EEG by a
clear decrease in global long-distance connections which are
progressively replaced by an intensification of local cortico-cortical
connectivity . On the other hand, MRI
connectivity analyses tend to show that the spatial extent of the
networks is preserved during the early phases of sleep . It is
necessary to explore this ap-
parent paradox in order to better understand the cerebral mechanisms at
play during the descent into sleep but also more broadly to explore the
human consciousness. . .
The project is based on data from a cohort being currently acquired. It
includes EEG and MRI acquisitions performed while the subjects are
falling asleep for
a nap. In a first step, the candidate will study the dynamic evolution
of the functional connectivity measured in EEG as a function of the
(spectral coherence, synchronization probability, phase synchronization
method, etc.). In a second step, these results will have to be compared
to those obtained in MRI by taking into account the physical
characteristics of the different signals.
In both cases (EEG/MRI) the correlations between the different areas
will be measured using positive defined matrices measuring the
correlation of the signals. For the EEG, these correlations can be
measured directly from the temporal signals or from time-frequency
analyses. In a first step, and for the EEG, we will have to characterize
the matrices corresponding to the different phases of sleep using the
calculation of averages on the variety of positive defined matrices.
In a second step, we will try (in EEG as well as in fMRI) to design
recurrent networks on such matrices  in order to automatically
classify the sleep phases as the acquisition progresses.
• The candidate must have a recent Ph.D. (within 5 years) in Computer
Science (or Applied Mathematics) in the field of Machine Learning.
• Knowledge and experience within Deep Learning frameworks is recommended.
• The candidate will perform research and algorithmic developments and
solid programming skills are required.
• Interpersonal skills and the ability to work well individually or as a
member of a project team are recommended.
• 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.
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
many old quarters, a population of around 120,000 the city area has
roughly 250,000 inhabitants.
Interested candidates should submit their application to
• luc.brun at ensicaen.fr and
• olivier.etard at unicaen.fr
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.
1 M. Bouchard, J.-M. Lina, P.-O. Gaudreault, J. Dubé, N. Gosselin, et J.
rier, EEG connectivity across sleep cycles and age, Sleep, p. zsz236, nov.
2019, doi: 10.1093/sleep/zsz236.
2 E. Tagliazucchi et E. J. W. van Someren, The large-scale functional con-
nectivity correlates of consciousness and arousal during the healthy and
pathological human sleep cycle, NeuroImage, vol. 160, p. 55-72, oct.
2017, doi: 10.1016/j.neuroimage.2017.06.026.
3 Nguyen, Xuan Son, Brun, Luc, Lezoray, Olivier & Bougleux, Sebastien. A
Neural Network Based on SPD Manifold Learning for Skeleton-Based
Hand Gesture Recognition. In The IEEE Conference on Computer Vi-
sion and Pattern Recognition (CVPR) , June 2019 .
4 Nguyen, Xuan Son, Brun, Luc, Lezoray, Olivier & Bougleux, Sébastien.
Skeleton-Based Hand Gesture Recognition by Learning SPD Matrices with
Neural Networks. In Proceedings of the 14th IEEE International Confer-
ence on Automatic Face and Gesture Recognition (FG 2019) 2019 .
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