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Phd Student

vor 3 Monaten


Hannover, Niedersachsen, Deutschland Georg-August-Universität Göttingen Vollzeit

PhD student (f/m/d)
This position is available starting from November 1st, 2024. It is a part time position with a limited contract of 3 years. Salary and working hours will be according to the German grade 13 TV-L (65%, currently 25,87 hours per week).

The position will be associated with the group of Prof. Dr. Michael Wibral. We are recruiting a PhD student for the interdisciplinary

research project "Evolutionary Convergence of Hierarchical Information Processing" funded by the German Research

Foundation (DFG) within the research initiative SPP2205 "Evolutionary optimization of neuronal processing". The project

aims to establish the common information theoretic principles of computation in hierarchical neural systems that are

conserved across species (in particular mammals and birds).

Specifically, we will use the analysis of information theoretic timescales [1], the correlation of information storage and transfer [2] and the analysis of unique, redundant and synergistic information [3,4] in order to take a comparative approach to neural computation in the primate and the avian brain.

The project will be pursued in close collaboration with the research groups of Viola Priesemann at the Max Planck Institute for Dynamics and Self-Organization in Göttingen and Onur Güntürkün, Jonas Rose and Roland Pusch at the Ruhr University of Bochum.

letter of interest, your curriculum vitae with a list of publications, copies of your certificates and contact information of at

If you have any questions, please contact
Prof. Dr. Michael Wibral

Please note:

[1] Rudelt et al Embedding optimization reveals long-lasting history dependence in neural spiking activity

[2] Wollstadt et al Information-theoretic analyses of neural data to minimize the effect of researchers' assumptions in predictive coding studies. PLOS Comp Biol e1011567

[3] Gutknecht et al Bits and pieces: Understanding information decomposition from part-whole relationships and formal logic. Proc. Royal Soc. A ,

[4] Ehrlich et al A measure of the complexity of neural representations based on partial information decomposition, Transactions on Machine Learning Research 5