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Annonce

26 mai 2020

PhD candidate in Neuromorphic Vision at Université Côte d'Azur in France


Catégorie : Doctorant


This PhD proposal takes part in the European CHIST-ERA APROVIS3D project (April 2020-March 2023) on the topic of bio-inspired machine learning for stereo event-based vision, using mixed analog-digital hardware.

The objective of the work is to design and implement Spiking-Neural-Network-based machine learning methods to extract vision features and infer useful information about the visual scene from stereo event-based cameras. In particular, the work will focus on two use cases: scene segmentation and depth estimation, and a demonstrator is expected, that will be benchmarked at software level for comparison with standard frame-based approaches in terms of precision and energy consumption, before its hardware integration.

The work will be carried out in collaboration with a leading neuroscience institute in Marseille, the Institute of Neuroscience of la Timone, that will be part of the supervision team.

See https://i3s.unice.fr/jmartinet/sites/default/files/u47/phd_description.pdf and https://www.chistera.eu/projects/aprovis3d

 

Dear colleagues,

We are seeking a PhD candidate Neuromorphic Vision starting between now and October at I3S lab, Sophia Antipolis, Université Côte d’Azur in France.
The applicant should hold a Master in Computer Science or Signal and Image Processing with background in Statistics and Applied Mathematics; skills in Python, C/C++; fluency in written/oral scientific english.

Application deadline on June 14, 2020 (please send a CV, cover letter, all available Master grades and ranking, Master thesis if available, any recommendation letter and supporting document). Video interview will take place from June 15.

More information: https://i3s.unice.fr/jmartinet/sites/default/files/u47/phd_description.pdf and https://www.chistera.eu/projects/aprovis3d

Summary:
This PhD proposal takes part in the European CHIST-ERA APROVIS3D project (April 2020-March 2023) on the topic of bio-inspired machine learning for stereo event-based vision, using mixed analog-digital hardware.
The objective of the work is to design and implement Spiking-Neural-Network-based machine learning methods to extract vision features and infer useful information about the visual scene from stereo event-based cameras. In particular, the work will focus on two use cases: scene segmentation and depth estimation, and a demonstrator is expected, that will be benchmarked at software level for comparison with standard frame-based approaches in terms of precision and energy consumption, before its hardware integration.
The work will be carried out in collaboration with a leading neuroscience institute in Marseille, the Institute of Neuroscience of la Timone, that will be part of the supervision team.
 
With kind regards,
Jean Martinet-
 
jean.martinet@univ-cotedazur.fr
i3s.unice.fr/jmartinet
+33.6.59.69.11.91
 

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