From Multi-Scale Modeling of Zoonotic Infections in Poultry Farms to Early Warning and Hierarchical Control of Avian Influenza in the French Antilles

This PhD thesis focuses on the development of mathematical and computational approaches for the early detection and control of avian influenza in poultry farming systems in the French Antilles. The project will build on a multi-scale modeling framework combining within-host infection dynamics, within-farm transmission, and territorial spread between farms. The objective is to design early-warning tools based on heterogeneous data, including farm typology and sensor-derived indicators, and to study targeted intervention strategies capable of limiting epidemic amplification. By combining epidemic modeling, state estimation, spatial dynamics, and control theory, the thesis aims to contribute to a regional decision-support framework for the surveillance and management of zoonotic risks.

For more information about the TALEDZ project, please consult:
https://taledz.univ-antilles.fr/pages/homepage.html

For any questions or applications, please contact:
 

The PhD will be hosted at Université des Antilles, on the Schoelcher campus in Martinique, within a multidisciplinary research environment connected to the TALEDZ project. The host laboratory is L3MA – Laboratoire des Matériaux et Molécules en Milieu Agressif, in collaboration with researchers from LAMIA – Laboratoire de Mathématiques, Informatique et Applications. This environment brings together expertise in applied mathematics, epidemiological modeling, data analysis, and control theory, with a strong focus on regional challenges related to animal health, zoonotic disease surveillance, and epidemic risk management in the French Antilles

The candidate should hold a Master’s degree in Applied Mathematics, Control Theory, Computational Biology, Spatial Epidemiology, or a related field. They should have strong quantitative skills, a solid background in at least one area such as dynamical systems, spatial modeling, deterministic state estimation, or optimal control, and good programming abilities in Python or MATLAB. The ideal candidate will be motivated, autonomous, and interested in multidisciplinary research at the interface of mathematics, epidemiology, data analysis, and animal health