Post-Doctoral Research Visit F/M Representation Learning and Clustering of Interacting Timed Systems

Contexte et atouts du poste

Scientific Context
Recent advances in computer vision and sensor technologies enable the capture of rich, finely time-resolved information on individual behaviors. From video recordings and multimodal sensors, it is possible to detect behavioral state changes and estimate interactions between individuals within a group. These observations provide essential data for modeling both individual and collective dynamics.
From an interpretability perspective, Timed Automata (TA) can be used as a symbolic representation framework for behavioral states and their temporal transitions. While learning a timed automaton for an individual over a single period is feasible, applying this approach to long-duration or repetitive behaviors involving multiple individuals quickly becomes uninformative and difficult to interpret.
The objective of this postdoctoral position is therefore to define a “typical behavior” for each individual wh...