Annoucement of a Ph-D thesis "Random matrices, diversity and coevolution" M/F

European Commission; France; Sur place; EUR 25 000 - 30 000; Plein temps

Résumé du poste

A leading research laboratory in France seeks a PhD candidate to explore evolutionary dynamics using Large Random Matrix Theory. The project involves interdisciplinary collaboration and aims to enhance current ecological models. Ideal candidates should have a strong background in applied mathematics and probability.

Qualifications

• Strong background in applied mathematics and probability.
• Some knowledge of mathematical biology or theoretical ecology.

Responsabilités

• Apply Large Random Matrix Theory to ecological systems.
• Extend models to include many species and traits.

Connaissances

Applied Mathematics

Probability

Mathematical Biology

Theoretical Ecology

Formation

Master Level

Description du poste

Organisation/Company: CNRS

Department: Laboratoire d'Informatique Gaspard-Monge

Research Field: Physics

Researcher Profile: First Stage Researcher (R1)

Country: France

Application Deadline: 4 Jun 2025 - 23:59 (UTC)

Type of Contract: Temporary

Job Status: Full-time

Hours Per Week: 35

Offer Starting Date: 1 Sep 2025

Funding: Not funded by a EU programme

Research Infrastructure Staff Position: No

Offer Description

The PhD thesis will take place at the "Laboratoire d'Informatique Gaspard Monge" (LIGM, UMR 8049), at Gustave Eiffel University, near Marne La Vallée (accessible via the RER A train at "Noisy Champs").

The advisors will be François Massol, an expert in theoretical ecology, and Jamal Najim, an expert in large random matrices.

This project aims to apply Large Random Matrix Theory to understand the evolutionary dynamics of large ecological systems, extending current models to include many species and traits, and exploring concepts like diversification and diversity constraints.

The team includes interdisciplinary researchers from mathematics, physics, and ecology, with prior successful supervision of related PhD projects.

The ideal candidate should have a strong background in applied mathematics and probability (master level) and some knowledge of mathematical biology or theoretical ecology.

A detailed project description is available.