{"id":20846,"date":"2025-07-01T15:08:30","date_gmt":"2025-07-01T13:08:30","guid":{"rendered":"https:\/\/www.egce.universite-paris-saclay.fr\/?p=20846"},"modified":"2025-07-01T17:02:11","modified_gmt":"2025-07-01T15:02:11","slug":"msc-internship-opportunity-2","status":"publish","type":"post","link":"https:\/\/www.egce.universite-paris-saclay.fr\/?p=20846&lang=en","title":{"rendered":"MSc Internship Opportunity"},"content":{"rendered":"\n

Modelling and Detection of Eco-Evolutionary Feedback Loops<\/h2>\n\n\n\n

Eco-evolutionary feedback loops describe the reciprocal interactions between the genetic adaptation of organisms and the biotic or abiotic environment in which they evolve. While environmental components such as temperature or competition impose natural selection on morphology, physiology, and behavior, organisms in turn modify the environment through their abundance and traits, influencing ecosystem functioning and stability.<\/p>\n\n\n\n

Despite their theoretical and applied relevance, these feedbacks remain poorly understood. Few theoretical or statistical tools currently exist to detect them and to predict their consequences on population dynamics and trait evolution.<\/p>\n\n\n\n

This project focuses on a potentially major feedback loop involving body size and population density. Body growth rate and adult size are key fitness-related traits influenced by natural selection. However, population density also affects body size, either via plastic responses or by modifying the evolutionary optimum through competition intensity.<\/p>\n\n\n\n

The intern will develop a theoretical model that couples ecological dynamics (demography) and quantitative genetics to simulate this eco-evolutionary feedback. This model will serve as the basis for constructing a statistical tool to detect such feedbacks using empirical time series data available in the hosting lab. The model will be implemented and analyzed using R.<\/p>\n\n\n\n

We are looking for a motivated MSc student, with training in evolutionary ecology, genetics, or statistical modeling. Interest in theoretical approaches is essential. Some familiarity with R is desirable but not mandatory. A continuation through a PhD program (statistical applications of the model through Bayesian \/ machine learning approaches) is possible.<\/p>\n\n\n\n