Post doctoral position on time series analysis of data living on the SPD manifold. Application to th

01/05/2022 – 02/05/2022 all-day

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : GREYC UMR 6072
Durée : One year
Contact :
Date limite de publication : 2022-05-01

Contexte :
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.

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.

Sujet :
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.

Profil du candidat :
Candidate profile

•  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.

Formation et compétences requises :
• 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.

Adresse d’emploi :
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.

Document attaché : 202112011007_postdoc_en2.pdf