CONTEXT
Animal behavior exhibits large inter- and intra-individual variability, reflecting complex interactions between internal neural dynamics and external signals from the environment. We aim to frame this variability as an adaptive feature in unpredictable environments and to understand how neuromodulation may drive the emergence of inter-individual variation throughout development. Addressing this challenge requires the development of data-driven and theoretical tools to quantify differences between dynamical systems from time series observations.
Building upon our recent work, we are looking for a MSc intern to work with our team on the development and application of data-driven approaches, grounded in statistical physics and dynamical systems theory, to analyze inter-individual variability in animal behavior.
This project will be carried out within the framework of the ANR project FORE-SIGHT. The intern will be integrated into the SIBBIL team and will have the opportunity to interact with PhD students and postdoctoral researchers in an interdisciplinary and international research environment.
MAIN MISSIONS
The main goal of this internship is to compare data-driven approaches for the analysis of variability among time series of animal behavior.
The intern will explore the performance of different modelling and inference techniques in terms of their robustness, interpretability, and reliability to noise.
Under the supervision of Antonio Carlos Costa, the intern will be in charge of the following tasks:
- Benchmarking different data-driven methods for measuring distance among time series observations from behaving animals
- Evaluate the limits of different approacchs, and the conditions in which they work best
Rencontrez Dominique Chiter, Data Manager
Rencontrez Céline, Chercheuse, Médecin déléguée, co-directrice du Centre d'investigations cliniques
Ces entreprises recrutent aussi au poste de “Données/Business Intelligence”.