[visionlist] Post doctoral position on time series analysis of, data living on the SPD manifold.

[Luc Brun]

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.


*Context*

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.

*Objectives and challenges*

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.
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.



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.

*Candidate profile*



  *

      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 highly
    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.

*Location*


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. Some photos 
<https://caen.maville.com/info/detail-galerie_-Caen-en-images-_344_GaleriePhoto.Htm>

*Application*

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.

*Additional information*


*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.

*References*


[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.
[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.
[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.
[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.

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