Animal behavior exhibits substantial 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 data-driven and theoretical tools to quantify differences between dynamical systems from time series observations.

Building on our recent work, we are seeking a MSc intern to join our team to develop and apply data-driven approaches, grounded in statistical physics and dynamical systems theory, to analyze inter-individual variability in animal behavior.

This project will be conducted within the framework of the ANR FORE-SIGHT project. 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

Under the supervision of Antonio Carlos Costa, the intern will be responsible for the following tasks:

• Benchmarking different data-driven methods for measuring distance among time series observations from behaving animals
• Evaluating the limits of different approaches, and the conditions in which they work best