Eco-evolutionary modelling in Avignon, France

Qualifications:
As part of the BackOut ANR project (Eco-evolutionary feedback loops out from the laboratory), we hire a postdoc having strong quantitative training, experience in statistical modelling and scientific programming, and a PhD in ecology, evolution, biostatistics, applied mathematics, or in a closely related field. The position is especially well suited to candidates interested in hierarchical and Bayesian modelling, eco-evolutionary dynamics, quantitative genetics, population dynamics, and the development of mechanistic inference from complex biological data.

Project description:
BackOut seeks to understand whether, how, and when eco-evolutionary feedback loops shape the stability and functioning of natural systems. These feedback loops arise when ecological conditions, such as population density or environmental variation, influence selection and evolution, while the resulting phenotypic changes in turn affect population dynamics, species interactions, and ecosystem functioning. Although such reciprocal links are increasingly recognised as central to the dynamics of populations and communities, they remain extremely difficult to quantify in natural systems from observational data alone. BackOut addresses this challenge by developing new inferential frameworks to recover these feedbacks from empirical data.

(1) The postdoc will develop and apply extended capture-recapture animal models (CRAMs) to investigate body-size-dependent eco-evolutionary feedback loops in brown trout. CRAMs combine capture-recapture approaches, which account for imperfect detection while estimating survival and population density, with animal models from quantitative genetics, which infer breeding values and the additive-genetic component of phenotypic variation. In the trout system, the aim is to integrate capture histories, phenotypic measurements, incomplete growth information, and pedigree data within a single hierarchical framework in order to quantify both directions of the feedback loop: how ecological conditions shape selection, and how phenotypic variation in turn affects fitness and population dynamics.

(2) The postdoc will also link individual-based inference in CRAMs to population-level inference based on mechanistic difference-equation models designed to recover hidden eco-evolutionary feedbacks from simpler abundance-and-phenotype time series. This line of research will build on recent mechanistic-statistical and neural differential equation approaches. In practice, the postdoc will work at the interface between detailed individual-based data and lower-dimensional time-series inference, helping to connect the direct estimation of feedbacks from capture-recapture-with-pedigree data with their indirect reconstruction from mechanistic dynamical models.

(3) Finally, the postdoc will contribute to the development of related CRAM approaches for crayfish data generated within the project and, in close interaction with project members, will take part in the broader cross-system comparison involving trout, crayfish, and medaka time series. This comparative perspective is an important component of BackOut, whose ambition is not only to estimate eco-evolutionary feedbacks in a single empirical system, but also to assess the generality of the underlying mechanisms and inferential tools across contrasted biological contexts.

Position details:
This is a full-time, 20 months position, with the potential to extend pending additional funding.
The successful candidate will be based at INRAE BioSP in Avignon, where we develop mathematical and statistical methods for spatial and spatio-temporal processes, notably in ecology and evolutionary ecology. The postdoc will be co-supervised by Julien Papaïx and Eric Edeline, and will work in close connection with Eric Edeline’s group at INRAE DECOD in Rennes. The project will also include interactions with the broader BackOut consortium and trips to Oslo to work with Asbjørn Vøllestad’s group, where the trout dataset was collected and where long-term knowledge of the study system has been developed.

Salary:
The monthly salary will be around €3,500 gross (approximately €2,800 net) and will be set according to the current INRAE salary scale, depending on the candidate’s experience.

Applications:
Send a CV and a cover letter to Julien Papaïx (julien.papaix@inrae.fr) and Eric Edeline (eric.edeline@inrae.fr) before June 21st 2026.