Post Doc in Genomics of Adaptive Divergence

Ludwig Maximilians University of Munich Faculty of Biology

2-Year Post-Doc Position in the Genomics of Adaptive Divergence

Institution: Faculty of Biology

Occupation date: as soon as possible

Application deadline: December 31, 2020

Salary group: E13

Time limit: 2 years

There is basically the possibility of part-time employment.

The Ludwig Maximilians University of Munich (LMU) is one of the most renowned and largest universities in Germany.


A postdoc position investigating the effect of gene flow on adaptive divergence in experimental populations of the fission yeast Schizosaccharomyces pombe is available in the research group of Jochen Wolf at Munich University, Germany.


Adaptive divergence describes how new forms can arise from a shared common ancestor by adaptation to different environments and is thought to be essential for the formation of new species by means of natural selection. Under geographical isolation, adaptive divergence readily results as a by-product of ecological specialization. Under conditions of gene flow, however, the conditions under which divergence may arise is a matter of debate. Gene flow might breakup beneficial combinations resulting in generalist phenotypes, but on the other hand can introduce novel variation that might facilitate novel adaptations. Due to the complex interactions between local adaptation, life history trade-offs, and genetic interactions, determining the mechanisms leading to divergence in natural systems is challenging. Controlled, replicated evolution experiments are a promising, yet largely unexplored way, to generate insight on the genetic basis of adaptive divergence in the context of gene flow.

The Project

We have been running an experimental evolution study for six years using the haploid fission yeast Schizosaccharomyces pombe, in which we vary the amount of migration while applying disruptive selection. The first analyses of the 132 replicate populations after 53 asexual generations (3 years into the experiment) showed divergence to be strongest in allopatry as would be expected. Yet, also with the highest levels of gene-flow divergent ecological adaptation arose (1). In this project we will further analyse the populations (currently at 150 sexual generations, including ~2000 asexual generations). We are specifically interested to understand how genetic correlations (trade-offs) between life history traits affect ecological adaptation and which genetic architecture the stable maintenance of divergence against gene flow. We will analyse time series and haplotype data to understand if and how divergent phenotypes are maintained over time and test hypotheses such as antagonistic pleiotropy, negativeepistasis, and assortative mating. This experiment with its stored longitudinal collection of population samples, which can be revived any time, is a great resource and evolution playground for any evolutionary geneticist.



The successful applicant holds a PhD degree, preferably with experience in experimental evolution, population genetics, comparative genomics, and has the bioinformatic skillset to analyse large genomewide data sets. Basic knowledge of molecular biology techniques are expected, but specific training in yeast-genetics and -genomics will be provided. The position is open to researchers willing to perform both experimental and bioinformatic/population genetic analyses. Previous experience with yeast, quantitative genetics and/or statistical modelling (e.g. linear mixed models) is a clear asset.

Research environment of the host lab

The Wolf lab applies an integrative approach to explore micro-evolutionary processes and genetic mechanisms underlying species divergence, adaptation and genome evolution (2, 3). Using large-scale genomic approaches combined with field and lab-based experiments, we characterize genetic diversity within and between populations and assess its relationship to phenotypic divergence (4–6) – sometimes interpreting the data under a conservation angle (7, 8). In addition, we explore methodological aspects of data analyses (9, 10) and engage in comparative approaches to study evolution across larger timescales (11, 12). Empirical systems currently include natural populations of birds (swallows, cuckoos and corvids (4–6, 13, 14)), marine mammals (pinnipeds and killer whales) (15, 16) and fission yeast (1,17, 18). More information on the research activities in the lab can be found at

The University of Munich is consistently ranked among the top Universities worldwide, in particular the life science branch with its newly inaugurated campus offering excellent technical facilities and many interaction possibilities including the gene center, several Max-Planck-Institutes and the Helmholtz Centre ( With the highest concentration of supercomputing in Germany the Leibniz Supercomputing Centre and its local partners provide access to state-of-the art computing facilities ( Munich, Bavaria’s capital, is a vibrant, yet relaxed city with many traditions still alive, a high quality of living and the Alps nearby.

Wir bieten Ihnen eine interessante und verantwortungsvolle Tätigkeit mit guten Weiterbildungs- und Entwicklungsmöglichkeiten. Schwerbehinderte Personen werden bei ansonsten im Wesentlichen gleicher Eignung bevorzugt. Die Bewerbung von Frauen wird begrüßt.

Weitere Informationen


Applications including a CV, a statement of motivation and the contact details of at least two references in a single .pdf should be sent to Please use ‘experimental divergence position’ as subject header. The position remains open until filled. Starting date as soon as possible, since disposition of funding terminates by the end of 2022. The current funding situation for basic research in Germany is good, such that realistic funding options, which we successfully exploited before, exist to secure additional funding beyond the duration of the current funding period.


Prof. Jochen Wolf

Literature reflecting the lab’s interests
1. S. Tusso et al., bioRxiv (2020).,Nat. Ecol. Evol (in press)
2. J. B. W. Wolf, H. Ellegren, Nat. Rev. Genet. 18, 87–100 (2017).
3. J. V. Peñalba, J. B. W. Wolf, Nat. Rev. Genet., 1–17 (2020).
4. U. Knief et al., Nat. Ecol. Evol. 3, 570–576 (2019).
5. J. W. Poelstra et al., Science. 344, 1410–1414 (2014).
6. N. Vijay et al., Nat. Commun. 7, 13195 (2016).
7. A. B. A. Shafer et al., Trends Ecol. Evol. 30, 78–87 (2015).
8. C. R. Peart et al., Nat. Ecol. Evol. 4, 1095–1104 (2020).
9. N. Vijay, J. W. Poelstra, A. Künstner, J. B. W. Wolf, Mol. Ecol. 22, 620–634 (2013).
10. A. B. A. Shafer et al., Methods Ecol. Evol. 8, 907–917 (2017).
11. B. Nabholz, H. Ellegren, J. B. W. Wolf, Mol. Biol. Evol. 30, 272–284 (2013).
12. A. D. Foote et al., Nat. Genet. 47, 272–275 (2015).
13. J. A. C. von Rönn, A. B. A. Shafer, J. B. W. Wolf, Mol. Ecol. 25, 2529–2541 (2016).
14. V. E. Kutschera et al., Mol. Biol. Evol. 37, 469–474 (2020).
15. A. D. Foote et al., Nat. Commun. 7:11693, 1–12 (2016).
16. C. P. Peart et al., Nat. Ecol. Evol., 4:1095-1104 (2020)
17. S. Tusso et al., Mol. Biol. Evol. 36, 1975–1989 (2019).
18. B. P. S. Nieuwenhuis et al., Nat. Commun. 9, 1639 (2018).

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