PhD Student (M/F) in Theoretical Evolutionary Biology: Modeling chromosomal inversions and local adaptation

Job Description

Thesis Subject

Context

Chromosomal inversions play a key role in local adaptation by suppressing recombination, which allows advantageous allele combinations to be maintained together (co-adaptation). However, this suppression of recombination comes at a cost: it promotes the accumulation of recessive deleterious mutations within the inversions. This genetic load can generate overdominance (an advantage for heterozygotes), which stabilizes inversions at intermediate frequencies in populations, regardless of their adaptive content.

The PhD student will develop a theoretical framework in which multiple inversions, each with its own explicit genetic content, interact within spatially structured populations. The challenge is to understand how these rearrangements evolve not only in response to divergent selection, but also in response to the feedback loops generated by their own mutational load and by linkage disequilibrium with other regions of the genome. This PhD project is part of the ANR JCJC CREADIV project ("Chromosomal rearrangements and diversification: building a unified theoretical framework"), funded by the French National Research Agency (ANR) and coordinated by Thomas Aubier.

Objectives

The main objective of the PhD is to develop and analyze theoretical models (analytical and numerical) to understand the emergence and maintenance of inversion polymorphisms. The work will focus on the following axes:
• Modeling the genetic load: Quantifying how mutations accumulate within inversions and how their overdominance emerges depending on parameters such as inversion length and mutation rate.
• Interactions between inversions: Studying how an inversion involved in local adaptation can facilitate (or hinder) the establishment of other inversions, in particular by examining the non-random associations that emerge between rearrangements.
• Dynamics of local adaptation: Analyzing how these feedbacks between genetic content and recombination suppression modify classical predictions on the formation of "supergenes" or islands of divergence under conditions of gene flow.
• Theoretical synthesis: Producing a unified framework to predict the genomic signatures of these interactions, in order to provide testable hypotheses for the empirical collaborators of the project working on natural systems (seaweed flies, Heliconius butterflies).

Activities

• Develop analytical theoretical models of population genetics (within the Barton-Turelli multi-locus framework) simultaneously integrating the evolution of genetic loci and chromosomal rearrangements.
• Develop and analyze individual-based (agent-based) models programmed in SLiM or C++ to test the robustness of analytical results (e.g., accounting for genetic drift, environmental stochasticity, and double crossovers).
• Study the complex feedback loops between the evolution of genetic load within inversions and the spatial dynamics of multiple inversions during local adaptation.
• Actively collaborate with empirical researchers in the consortium (C. Mérot on the seaweed fly Coelopa frigida, M. Joron on the Heliconius numata butterfly) to contrast theoretical predictions with evolutionary genomics data.
• Write scientific articles for peer-reviewed international journals.
• Present results at international seminars and conferences (e.g., ESEB).

Your Work Environment

The PhD student will join the CRBE (Centre de Recherche sur la Biodiversité et l'Environnement) located in Toulouse. The institute offers an exceptional and stimulating scientific environment, bringing together numerous experts in ecology and evolution.
Under the direct supervision of T. Aubier, the PhD student will be part of a newly formed team dedicated to the study of biological diversification.
The CREADIV project benefits from a vast network of national and international collaborators (including the University of North Carolina and the University of Vienna), offering the candidate real opportunities for mobility (funds are planned for participation in several international conferences).

Expected skills

Knowledge and technical skills:
• Master's degree (or engineering degree) in Evolutionary Biology, Ecological Modeling, Applied Mathematics in Biology, or Bioinformatics.
• Solid understanding of the basic concepts of evolutionary theory and population genetics.
• Proven programming skills (SLiM or C++ highly desired, or ability to learn quickly) and/or symbolic/numerical computation (Mathematica, R, Python).
• Fluency in scientific English (reading, writing, speaking) is essential to interact with the project's international network.

Soft skills:
• Strong autonomy and intellectual rigor.
• Scientific curiosity and a taste for interdisciplinarity (ability to bridge mathematical theory and empirical biological data).
• Ability to work within a dynamic and collaborative team.

Application procedure

The application and all requested documents (below) must be submitted on the emploi.cnrs.fr portal.
Detailed CV - a cover letter - academic transcripts for Master 1 and Master 2 (or equivalent) - the name and email address of two references.

An initial selection will be based on the evaluation of the submitted documents, followed by an interview before a jury where the candidate will present their experience and how it will be useful to successfully carry out the PhD project.

Compensation and benefits

Compensation

2300 € gross monthly

Annual leave and RTT

44 jours

Remote Working practice and compensation

Pratique et indemnisation du TT

Transport

Prise en charge à 75% du coût et forfait mobilité durable jusqu’à 300€

About the offer

About the CNRS

The CNRS is a major player in fundamental research on a global scale. The CNRS is the only French organization active in all scientific fields. Its unique position as a multi-specialist allows it to bring together different disciplines to address the most important challenges of the contemporary world, in connection with the actors of change.

CNRS

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