Postdoc (or PhD) position

We are seeking a motivated Postdoc for joining our Research Group for Mathematical and Computational Population Genetics, at the University of Tübingen. If you are interested in mathematical and/or computational approaches to model and understand microbial evolution or would like to learn about it, this is your opportunity.
Your contribution will make a significant difference in our young and agile research group.

The research of the 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. The group is currently set up and you can play a major role in the development of this group.

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 that are based on stochastic processes. Subsequently, often the goal is to come back to the data and to obtain applicable computational approaches for the biological data using the theoretical result. Therefore we implement inference, estimation, and analysis tools using phylogenetics, simulations, and machine learning approaches to analyze human and prokaryotic genome data.

In addition to his very own projects, the PostDoc is offered the opportunity to co-supervise suitable Ph.D. 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 considered.
Examples of some of the issues that we are interested in furthering include (depending on the methodological background):

• Effective simulation of gene transfer in the pan-genome of 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 using diffusion theory and/or simulations

• Bet hedging, Dormancy, Gene Loss, and other strategies in microbial communities

• Create machine learning tools for population genetics

If you are not as keen on 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 autumn 2021, but the starting date is flexible and a remote start is also possible. The position comes without any formal teaching duties and can initially last for two years with possible extension.

Further details and the official job announcement can be found on the CMFI website.

Ph.D. position (PostDoc also possible)

Population genetic models for the interplay between CRISPR systems and plasmids in bacteria

Together with Hildegard Uecker at the MPI in Plön, I am looking for a Ph.D. student (d/f/m) interested to study mathematical models for the interplay between plasmids and CRISPR-Cas systems.
The project will be based in Tübingen but includes the possibility of multiple longer research visits at the MPI in Plön.
The Ph.D. project will be co-supervised by the two PIs Hildegard Uecker and Franz Baumdicker.

We are seeking a motivated candidate for joining our Research Group for Mathematical and Computational Population Genetics, at the University of Tübingen in a Ph.D. project on the population genetics of plasmids and CRISPR systems.

The revolutionary CRISPR/Cas technology to edit genomes has its origin in the natural CRISPR-Cas
defense systems of bacteria and archaea. These systems essentially work by cutting foreign
DNA (e.g. phage DNA) at specific spots. Some types of CRISPR are preferentially encoded on
plasmids, which are extra-chromosomal DNA elements. So-called conjugative plasmids can
horizontally transfer between bacterial cells and thus introduce the CRISPR system into other cells.
CRISPR systems have some remarkable properties that make them particularly interesting for both
theoretical as well as applied investigations. To identify specific foreign DNA, the CRISPR system
contains an array of spacers that align with these targets. This array is itself subject to evolutionary
forces including deletion, and selection. Based on their ability to target specific DNA sequences,
CRISPR systems could help to re-sensitize resistant pathogenic strains to antibiotic treatment --
they could be used to eliminate other plasmids that carry antibiotic resistance genes.

The aim of this project is to develop mathematical models for the eco-evolutionary dynamics of
plasmid-encoded CRISPR systems within the framework of theoretical population genetics and to
explore their potential as a therapeutic intervention against antibiotic-resistant bacteria. Possible
questions include: Do plasmids use CRISPR systems to compete with other plasmids? How does
the copy number of resistance plasmids affect the chance of successful elimination through mobile
CRISPR systems? How can we optimize the outcome of a combined antibiotic drug and CRISPR-based
re-sensitizing treatment?

On the mathematical side, the project involves stochastic modeling in combination with
deterministic approaches using branching processes as well as diffusion theory. An optional
computational side of the project can involve simulation-based inference as well as quantitative
analysis of sequenced CRISPR arrays on plasmids. Depending on the interests and profile of the
student, the focus can be more on analytical work or on computer simulations.

Applicants should have a degree preferably in mathematics or physics, or alternatively in computer
science, quantitative biology, or another related field. Good quantitative skills are essential.
Experience in mathematical modeling and knowledge of a programming language is an advantage.

Alternatively, it would also be possible to tackle this project as part of a PostDoc position in our group.

The position, paid according to TVL 13, is funded by the CMFI Cluster of Excellence in Tübingen and accordingly does not include teaching duties, but the opportunity to participate in teaching

Papers of interest:
Santer, M. and Uecker, H. (2020). Evolutionary rescue and drug resistance on multicopy plasmids.
Genetics 215(3): 847-868.
Baumdicker, F., Huebner, A.M.I., and Pfaffelhuber, P. (2018). The independent loss model with
ordered insertions for the evolution of CRISPR spacers. Theor. Popul. Biol. 119, 72–82.

If you have any questions about the project or the position, please feel free to write to Diese E-Mail-Adresse ist vor Spambots geschützt! Zur Anzeige muss JavaScript eingeschaltet sein!

Please apply by email to Diese E-Mail-Adresse ist vor Spambots geschützt! Zur Anzeige muss JavaScript eingeschaltet sein! with a subject line in the following style:
PhD (or PostDoc) Application -- CRISPR Evolution -- Your Name

Please send your application as a single PDF file and include a brief statement of your interests and experience, a CV (including a list of possible publications and contact information for two academic references), and university transcripts.

Your contribution will make a significant difference in our young and agile research groups. Research in the Baumdicker 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". Our work spans collaborations with biologists to mathematicians. We mainly use population genetics, diffusion theory, stochastic processes, and phylogenetics approaches. In all our projects we aim to obtain not only theoretical results but also applicable computational tools 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.

For more information about the Uecker Group collaborating on this project, visit http://web.evolbio.mpg.de/stochdyn/.

Besides individual mentoring and guidance for your individual career development, we provide detailed scientific training and open, community-focused science communication. Beyond scientific publications, this will include conference visits, organization and attendance of workshops, and possible research visits to other universities or international summer schools.

If you are not as keen on the evolution of CRISPR as we are, but would still like to join the group, please get in touch with us anyway. We might have further funding resources that might suit you. In this case, the main topic of the PhD project can be developed together in a discussion with the candidate.