05.03.2026, Studentische Hilfskräfte, Praktikantenstellen, Studienarbeiten

Explore how insects’ surface pheromones—used for waterproofing, communication, and mate choice—shift during adaptation to new environments. Working with experimental evolution lines of seed beetles, you’ll test how host plants alter chemical profiles and, potentially, pheromonal communication. Gain hands‑on experience with chemical analysis, evolution experiments, and multivariate data to study rapid trait evolution.

How do organisms rapidly adjust to new environments—and how does this reshape their communication, mating behavior, environmental adaptation? In this project, you will investigate how chemical surface profiles evolve during experimental host adaptation in seed beetles (Callosobruchus maculatus), which are a pest of stored legumes.
Using replicated experimental evolution lines, this project will test whether adaptation to different host environments drives predictable and repeatable changes in their surface chemistry and whether these shifts influence how beetles recognize and mate with each other.
You’ll get hands-on experience with:
- Experimental evolution and maintaining selection lines
- Analytical chemistry and chemical profiling of insects
- Multivariate statistical analysis of complex phenotypes
- Applying evolutionary theory to chemical data

This project tackles relevant evolutionary questions:
- How fast can complex traits evolve?
- Can ecological adaptation reshape chemical communication?
- Can host shifts trigger the early stages of reproductive isolation?

This project is well suited for students interested in evolutionary genetics, chemical ecology, and adaptation.

Time frame:
This project can be started year-round, start date flexible, project time can be adjusted according to the type of thesis, internship, "Forschungspraktikum” etc.

Requirements:
Self-sufficient, reliable and precise work ethic in the lab. Experience with analytical chemistry not required as this will be taught in the project, but some experience with data analysis through statistical programs such as R would be beneficial.

Kontakt: jan.buellesbach@tum.de