The FOX team from the CRIStAL laboratory (UMR CNRS), Lille France and the PR team from the MIS Laboratory, Amiens France are looking to recruit a joint PhD student for a project titled 'EventSpike - Asynchronous computer vision from event cameras'. The project aims to develop new models of spiking neural networks (SNN) capable of directly processing visual information in the form of spike trains for applications in autonomous driving. The thesis will focus on weakly supervised learning methods based on spiking learning mechanisms to exploit the flow of impulses generated by an event camera.

The FOX team of the CRIStAL laboratory (UMR CNRS), Lille, France, and the PR team of the MIS Laboratory, Amiens, France, are looking to recruit a post-doc starting as soon as possible and a joint PhD student starting in October 2025 in the field of asynchronous computer vision from event cameras. The main objective is to develop new models of spiking neural networks (SNN) capable of directly processing visual information in the form of spike trains. The proposed models must be validated experimentally on dynamic vision databases, following standard protocols and best practices. The PhD candidate will be funded for 3 years (grant application pending) and is expected to defend his/her thesis and graduate by the end of the contract. The monthly gross salary is around 2000€, including benefits. The post-doc will be hired for 18 months starting from March 2025 (this is a fully-funded position). The monthly gross salary is around 2500-3000€, including benefits.

The story of event cameras starts from the very beginnings of neuromorphic engineering with Misha Mahowald and Carver Mead. The chip design of these “silicon retina” cameras is the most crucial aspect that might enable them to come to mass production and widespread use. Once we have a usable camera is just the beginning, because now we need to think of our use of the data as though we were some type of artificial “silicon cortex”. That step has just started but the last few years have brought some remarkable results from the computer vision community. This talk will have a lot of live demonstrations.