Informations générales
Intitulé de l'offre : Design engineer (M/F): Modeling latent structures of neural connectomes (H/F)
Référence : UMR3571-CHRVES-004
Nombre de Postes : 1
Lieu de travail : PARIS 15
Date de publication : vendredi 1 août 2025
Type de contrat : IT en contrat CDD
Durée du contrat : 11 mois
Date d'embauche prévue : 1 février 2026
Quotité de travail : Complet
Rémunération : Between 2 571€ and 3 817€
Niveau d'études souhaité : BAC+5
Expérience souhaitée : Indifférent
BAP : A - Sciences du vivant, de la terre et de l'environnement
Emploi type : Ingenieure ou ingenieur biologiste en traitement de donnees
Missions
This project aims to characterize the latent structure of the neural circuitry of the neuronal networks of small animals. The work builds on a generative network modelling framework that we are developing in the lab based on embedding the nodes in a dual metric space where distances determine link probabilities based on a kernel that is learned from data. We aim to utilize these embeddings to discover latent topological structures of the networks and link these to neuronal roles and their real 3D space embedding as well as to other higher-order features such as circuit motifs and communities.
Activités
- Develop a statistical procedure for identifying significant latent features of neural connectomes from the positions of neurons in the learned latent space.
- Validate the methodology on synthetic networks and apply it to synapse-resolution connectomes of small animals.
- Compare the latent positions of neurons to the real space positions of their soma and relate the inferred latent features to topological features and to known neuron types, circuits, and clusters.
- Possibly extend the methodology to learnable kernel functions by leveraging kernel compositionability.
Compétences
We are looking for a highly motivated candidate with a strong quantitative background in either physics or applied mathematics (including but not limited to machine learning and statistics). The selected candidate will work in a highly interdisciplinary environment mixing physicists, biologists, and mathematicians.
Fluency in Python and numerical simulations is expected.
Contexte de travail
We rely in the lab mainly on the Drosophila melanogaster larva as model animal to study these questions. Its full connectome, containing ~12,000 neurons, has been mapped at synaptic resolution. Furthermore, a vast genetics toolbox makes it possible to target and control individual neurons in freely behaving animals. Large-scale screens have revealed the individual influence of thousands of neurons on the behavior in millions of larvae, and several microcircuits controlling specific behavioral decisions and actions have been identified.
The selected candidate will work in a highly interdisciplinary environment mixing physicists, biologists, and mathematicians.