PhD in computational neuroscience (M/F)

id="offer-description" class="ecl-u-type-heading-2 ecl-u-mt-none ecl-u-mb-l">Offer Description Visual scenes are structured at every scale, from a single blade of grass to larger objects and faces. Despite this complexity, the visual system combines these scales effortlessly, and does so on a power budget comparable to a lightbulb, against the megawatts required by modern artificial neural networks. This PhD project aims to understand how, using mathematical modelling drawn from information theory and statistical mechanics. The core of the project is to build a theory of multi-scale neural coding based on methods we have recently developed around diffusion models, the class of generative machine learning models used to synthesise natural images. The student will then test the predictions of this theory against recordings from visual neurons in the retina and cortex responding to natural scenes. The work will be carried out in close collaboration with experimental groups providing the neural data. Theory of visual perception across scales, from features to scenes. Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant/UMR7210-MATCHA-001/Default.aspx . Requirements Research Field Mathematics Education Level PhD or equivalent. Languages FRENCH Level Basic . Research Field Mathematics Years of Research Experience None. Additional Information. Website for additional job details https://emploi.cnrs.fr/Offres/Doctorant/UMR7210-MATCHA-001/Default.aspx. <h2