PhD student (M/F): Theoretical and statistical modeling of eco-evolutionary feedback loops from abundance-phenotype time series

CNRS; France; On-site; EUR 20,000 - 27,000; Full time

Job summary

CNRS recrute un/une doctorant(e) au sein de l’unité EGCE, Gif-sur-Yvette, sur le projet BackOut, supervisé par des chercheurs de CNRS et de partenaires internationaux. Le sujet porte sur les boucles d’éco‑évolution entre taille du corps et densité de population, avec des analyses statistiques et des modèles mécanistes et ML.

Le candidat développera des modèles, effectuera des analyses mathématiques et utilisera des outils statistiques avancés pour ajuster les modèles aux séries temporelles

Qualifications

• Master en mathématiques appliquées, biologie théorique ou écologie.
• Formation solide en programmation (R, BUGS/JAGS, Python, etc.) et en statistiques (Bayésien et fréquentiste).
• Connaissances en apprentissage automatique appréciées.

Responsibilities

• Développer des modèles théoriques et numériques en écologie évolutive.
• Réaliser des analyses mathématiques et des ajustements statistiques avancés sur des séries temps taille du corps/abondance.
• Comparer des modèles mécanistes avec des approches basées sur l'apprentissage automatique.

Skills

R programming

BUGS/JAGS

Python

Statistics

Education

Master en Mathématiques Appliquées

Master en Biologie Théorique

Master en Écologie

Tools

R

BUGS/JAGS

Python

Job description

Organisation/Company CNRS Department Évolution, génomes, comportement et écologie Research Field Biological sciences Environmental science Researcher Profile First Stage Researcher (R1) Application Deadline 5 Nov 2026 - 23:59 (UTC) Country France Type of Contract Temporary Job Status Full-time Hours Per Week 35 Offer Starting Date 1 Jan 2027 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

This PhD is already funded as part of the collaborative ANR BackOut project (Eco-Evolutionary Feedback Loops out from the Laboratory), which runs from 2025 to 2029 and involves 6 national and international partners. The PhD candidate will be co-supervised by Arnaud Le Rouzic (CNRS Researcher, EGCE, IDEEV, Université Paris-Saclay) and Eric Edeline (INRAE Research Director, DECOD, Rennes) and will benefit from interactions with other project partners , including Julien Papaïx (INRAE BioSP) and Willem Bonnafé (University of Oxford).
The PhD candidate will be based at the EGCE research unit in Gif-sur-Yvette, approximately 35 km south of Paris, on the new Paris-Saclay campus. EGCE is part of the Institute of Diversity, Ecology, and Evolution. Arnaud Le Rouzic's "Genomes and Evolution" group includes 8 researchers and approximately 15 international students and postdoctoral fellows. Regular in-person meetings with the co-supervisor will be organized.

Scientific Context

Eco-evolutionary feedback loops describe the reciprocal interactions between the genetic adaptation of organisms and their biotic or abiotic environment. While environmental components such as competition impose natural selection on morphology, physiology, and behavior, organisms in turn modify their environment through their abundance and traits, thereby influencing the competition acting on phenotypes.
Despite their theoretical and applied relevance, these feedback loops remain poorly understood empirically. Few theoretical or statistical tools currently exist to detect them from observational data in natural settings or to predict their consequences for population dynamics and trait evolution.

Project Presentation

This project focuses on a potentially major feedback loop between body size and population density. Body size is a key ecological trait likely to influence individual reproduction. In return, population density also affects body size, either through plastic responses or by altering the evolutionary optimum via competition intensity.

The project has a dual purpose, both fundamental and methodological:

• 1. From a fundamental perspective, determine the conditions under which eco‑evolutionary feedback loops emerge in natural environments, as well as their eco‑evolutionary consequences.
• 2. From a methodological perspective, assess sampling strategies and modeling tools to detect these loops.

The PhD candidate will develop empirically usable theoretical models, conduct mathematical and numerical analyses to explore model behavior, and use advanced statistical tools to fit models to body size‑abundance time series observed in natural conditions. Specifically, they will extend a basic eco‑evolutionary model recently developed by our consortium to account for increasing biological complexity (e.g., populations open to dispersal, overlapping generations, population structure) or for specific sampling effects, such as limited capture rates. The performances of mechanistic eco‑evoltionary models built from a priori assumptions will be compared with the performances of alternative machine learning‑based approaches that will put less emphasis on a priori assumptions.

Required Skills

We are seeking a candidate with a master's degree in applied mathematics, theoretical biology, or ecology, who is attracted to fundamental questions in ecology and evolution. Strong training in programming (R, BUGS/JAGS, Python, etc.) and statistics is required (Bayesian and frequentist approaches; knowledge of machine learning would be an asset).