Arne Traulsen

For further information please refer to <https://www.evolbio.mpg.de/person/12087/2169>
and for project 2 also to <https://yuriypichugin.github.io>.

If you have questions after having read the relevant literature, contact
for project 1: Arne Traulsen <traulsen@evolbio.mpg.de> or co-supervisor Cornelia Pokalyuk <cornelia.pokalyuk@uni-luebeck.de>
for project 2: co-supervisor Yuriy Pichugin <pichugin@evolbio.mpg.de>

To apply for the position(s) please write an email to <imprs-application@evolbio.mpg.de> for attention of Ms. Ellen Karl from the MPI personnel department. Your application documents have to be compiled in one PDF including a short motivational statement, a short CV (biosketch), bachelor and master degrees/transcripts of records and contact information for two academic references. Name the PDF as follows: Lastname_Firstname_Lastnamesupervisor.pdf. 

With submission of your application, you accept the processing of your applicant data in terms of data-protection law. For further information on the legal basis and data usage we refer to the MPG privacy policy on <https://www.evolbio.mpg.de/3246466/privacy-policy>
 

1. Stochastic spreading processes on dynamical random networks

Supervisors: Arne Traulsen (MPI), Cornelia Pokalyuk (University of Lübeck)

Many problems in ecology and ecology can be described as the spreading of a new genetic variant or a new trait in a structured population. For example, think of new genetic variants taking over a deme structured population or new disease variants spread in the human population. The situation is getting particularly interesting when the spreading process dynamically modifies the population structure itself, such as in epidemiology, where infected individuals may temporarily have fewer contacts. Tackling such processes mathematically is challenging, especially when stochastic effects are taken into account. However, a mathematical understanding of limiting cases can be very helpful to shed light also on more complex dynamical regimes. This project will combine mathematical and computational approaches to model stochastic spreading processes with feedbacks between the spreading process and the underlying dynamical population structure.

This project is ideal for a candidate interested in combining rigorous mathematical approaches with computational simulations to describe biological processes.

Websites:

https://www.math.uni-luebeck.de/mitarbeiter/pokalyuk/forschung.php

https://www.evolbio.mpg.de/person/12087/2169

2. The evolution of multicellular life cycles

Supervisors: Arne Traulsen (MPI), Yuriy Pichugin (MPI)

The leap from unicellular to multicellular life was a transformative event that occurred independently multiple times, giving rise to the astonishing complexity of species such as animals, plants, and fungi. But what fundamental principles govern this transition? Our research investigates the evolution of multicellularity through the unifying lens of life cycle evolution.

A unicellular amoeba and a cow both begin life as a single cell. For the amoeba, cell division marks the end of its life cycle, producing two new individuals. For the cow, the first division of the zygote is just the beginning of an elaborate, coordinated developmental program. What evolutionary forces shape these radically different life cycles, and what are the consequences of these evolutionary changes?

Within this framework, the PhD project will involve developing theoretical models to address questions such as: What selective pressures drove the emergence of the first multicellular organisms? How did the diverse body plans of complex multicellular life evolve? Why did cell differentiation emerge in some multicellular organisms but not in others? What roles do environmental dynamics and ecological interactions play in the emergence of biological complexity?

This project is ideal for a candidate passionate about using mathematical modeling, computational simulations, and machine learning to answer deep questions in evolutionary biology. You will join a dynamic, interdisciplinary research group committed to basic science.

Websites:

https://yuriypichugin.github.io

https://www.evolbio.mpg.de/person/12087/2169