Job offers

You can find below current job offers in the lab.

We are currently recruiting an engineer to “Implement an open-source data hub of 3D cell reconstructions and meshes”.

We will recruit a PhD student in 09/2025. The job offer will soon be posted on the website.

We do not have open post-doc positions at the time. However, we will gladly assist early-career scientists in applying for fellowships, such as the MSCA Postdoctoral Fellowships.

Inria offers support to candidates who wish to apply to MSCA Postdoctoral Fellowships. More details can be found here, including a link to register for the information session that will take place on March 31st 2026 1-2 pm, Paris time. In this context, our team suggests two postdoc projects (see below). We are also open to any other projects that prospective candidates would like to work on.

Project 1. Astrocytes are glial cells of the central nervous system involved in numerous brain functions, such as the regulation of neurotransmission, synaptogenesis, and the maintenance of ionic and metabolic homeostasis. The goal of this computational work is to investigate how the structural and molecular variability of astrocytes affect information processing in the brain. This work will leverage recent high-resolution volume FIB-SEM electron miscroscopy and single-cell proteomics data of astrocytes from various brain regions, building upon models of astrocyte calcium signaling and neurovascular coupling from the team. The models, 3D meshes, and tools developed will be shared in open-access on a database currently being developed in the team, following the FAIR principles. As astrocytes contact hundreds of thousands of synapses simultaneously, this study could provide novel insights into computational principles of information processing in the brain.

Keywords: computational neuroscience, astrocytes, data-driven modeling, simulation, geometry, pde, fair

Project 2.

Computational modeling of biochemical processes in cells is an essential tool for understanding their functioning. In the case of brain cells named astrocytes, simulations are run to investigate the effect of cellular spatial properties on calcium dynamics, and thus on cell function. Our team has started to explore a machine learning (ML) method named partial differential equation (PDE) discovery to extract a PDE from simulated data and characterize calcium dynamics. This preliminary work does not yet allow discriminating the various signals observed in distinct sub-cellular microdomains. We propose to draw on principle from local ML explanation to suggest a PDE-based explanation of local dynamics. PDE discovery will provide simple PDEs to estimate and describe local signals. The postdoctoral researcher will develop an efficient ML tool to extract such local PDEs across the spatial domain and evaluate its performance on various simulated systems, including astrocytes.

Keywords: PDE discovery, machine learning, explanability, molecular simulation

If you are interested or curious, please do not hesitate to contact me!