Description du sujet de thèse

The goal of this multidisciplinary PhD thesis is to understand the interactions and dynamics of multiple co-circulating viruses within a host population with overlapping generations. The ambition is to deepen our understanding of virus-virus interactions through a population-structured host model. Host-based models offer an advantage: their parameters are often easier to calibrate and measure than those of models built directly at the pathogen scale.
The mathematical study will be complemented by an empirical case particularly suited to this approach: insect farms.
These microcosms are ideal for studying multi-virus dynamics because they allow large-scale experimentation on the factors driving viral population shifts towards epidemic outbreaks. Insect populations are abundant enough to justify density-based models, and their rapid generational turnover allows us to decouple disease dynamics (pathology) from pathogen evolution.
This PhD project consists of three main components:
Theoretical Objective 1: Define effective indices to capture ecological diversity in viral systems across a wide range of dynamical regimes. The aim is to extend classical descriptions to accommodate more complex dynamics, known to arise under certain epidemiological parameter settings. In particular, invasion graphs will be used to propose novel diversity measures and to accurately describe the most intricate scenarios.
Theoretical Objective 2: Apply the above diversity tools within the framework of large-scale generalized Lotka-Volterra systems, tailoring these established ecological models to the viral interaction context. Special attention will be paid to how epidemiological parameters influence microbial diversity.
Empirical Objective 3: Align theoretical developments with biological data. The PhD student will have access to both qualitative and quantitative viral diversity data from insect farms, in collaboration with IRBI. These data will serve to test model assumptions, validate numerical and theoretical results, and inform the design of specific experiments to confirm or challenge theoretical predictions.
Required degree: Master's degree in Applied Mathematics (Statistics, Probability, Dynamical Systems, or related disciplines).
Required skills:
(i) Strong theoretical background in modeling and statistical inference.
(ii) Solid knowledge of qualitative analysis of differential equation systems.
(iii) Proficiency in at least one programming language: R or Python.
Desired skills: Experience with Bayesian inference, complex systems, or random matrices.
A genuine interest in experimental sciences will be appreciated.

Contexte de travail

This multidisciplinary PhD project is part of the COIMuV project (Co-infections and Multi-Virus Interactions), which has received financial support from the CNRS through the MITI interdisciplinary programs.
The thesis will be jointly supervised by Hermine Biermé (40%) (probability and statistics), Professor at IDP, team SPACE, Elisabeth Herniou (20%) (evolutionary biology and insect virology) Director of Research at IRBI, team IMIP and Sten Madec (40%) (applied differential equations), Associate Professor at IDP, team EMS
The PhD candidate will be enrolled in the Doctoral School of Mathematics, Informatic, Theoretical Physics and Systems Engineering (MIPTIS - ED 551) from Tours University.