PostDoc or PhD position
Modelling the evolution of microbial pan-genomes, gene transfer, and microbial communities
We are seeking a motivated candidate for joining our Research Group for Mathematical and Computational Population Genetics, at the University of Tübingen.
The research in our group is primarily focused on mathematical and computational approaches to explain microbial evolution. We are part of two Clusters of Excellence in Tübingen, namely the Clusters "Controlling Microbes to Fight Infections" and "Machine Learning in Science". Thus there are plenty of opportunities to collaborate across various disciplines. The work spans collaborations with biologists to mathematicians.
Our research projects are usually inspired by observations in microbial genome data that are related to evolutionary questions. From there we try to identify the main forces responsible for the observed dynamics and create a model as complex as necessary, but as simple as possible. Therefore we rely on population genetics, diffusion theory, phylogenetics approaches, and other tools based on stochastic processes and analytical approaches. The goal in all projects is to come back to the data and to obtain applicable computational approaches for biological data. Therefore we implement inference, estimation, and analysis tools using phylogenetics, simulations, and machine learning approaches to analyze human and prokaryotic genome data.
A PhD candidate can be integrated into one of the graduate schools at the University of Tübingen
A PostDoc, in addition to his very own projects, is offered the opportunity to be responsible for suitable PhD projects within the group.
You can join our group with theoretical as well as computational backgrounds. Mathematicians, Bioinformaticians, and Computational Biologists, and related fields are welcome.
Examples of some of the issues that we are interested in furthering include (depending on the methodological background):
Effective simulation of gene transfer in microbial populations
CRISPR spacer distributions in microbial communities
Analyze branching processes that describe the benefit of HGT mediated gene gains
Investigate the evolution of evolutionary rates under fluctuating selection
- Create machine learning tools for phylogenetics
If you are not as keen about the topics above as we are, but would still like to join the group, please get in touch with us anyway. We might have further funding resources and topics that might suit you. In this case, the main topic of the project can be developed together in a discussion with the candidate.
The best time to start would be in summer 2021, but the starting date is flexible and as we are a remote-first team anyway a remote start is possible.
Further details and the official job announcement can be found on the CMFI website.