Quantitative Adaptation Dynamics in Microbial Populations
We study the role of feedback in driving learning-like adaptation in yeast populations and clonal dynamics under complex environmental conditions.
Projects in this area combine CRISPR editing, molecular biology, microfluidics, advanced microscopy, real-time measurements, and quantitative analysis. The work often involves building or modifying experimental setups, analysing large datasets. While the questions are quantitative in nature, they are motivated by biological phenomena, and we value students and postdocs who are interested in understanding both.
We welcome applicants from diverse backgrounds, including biology, physics, engineering, and related fields. Prior experience with the techniques we use is not required; what matters most is curiosity, persistence, and a willingness to engage with technically challenging problems. We aim to provide an environment in which trainees can develop both experimental and analytical skills while working on questions that sit at the interface of biology, dynamics, and evolution.