Postdoctoral Researcher in Neurotechnology (M/F)

id="offer-description" class="ecl-u-type-heading-2 ecl-u-mt-none ecl-u-mb-l">Offer Description To study the role of cortical circuits in the integration of somatosensory (tactile and proprioceptive) information essential for motor learning, our team has developed several innovative brain-machine interfaces adapted for use in the mouse model. These approaches are based on the real-time recording and analysis of single-neuron activity in the primary motor cortex (Arduin et al., 2013; Abbasi et al., 2018). In particular, we trained mice to control a custom-made forelimb prosthesis using such brain-machine interface (Ferrand et al., 2025). At the same time, we have identified strategies for transmitting somatosensory information to subjects using optogenetic tools, by performing patterned photostimulation of the primary somatosensory cortex (Lassagne et al., 2022). In particular, in closed-loop experiments, we provided artificial somatosensory feedback that was behaviorally relevant to facilitate motor control via a brain-machine interface (Abbasi et al., 2023). More recently, we have also developed a brain-machine interface capable of decoding motor intentions on a broader mesoscopic scale (Picard 2025), using optical signals recorded via wide-field calcium imaging at the surface of the cortex to control a prosthesis. The proposed research program aims to combine this “mesoscopic” decoding approach with optogenetic somatosensory feedback using a bidirectional all-optical strateg