Unsupervised Local Learning and Efficient Coding

Our group investigates learning and self-regulation in neural and social networks. By putting both side-by-side, we carve out the principles of self-regulation in living networks. The PhD / PostDoc projects are open topic. If you are interested to understand the principles or implementation of learning in living systems in the context of efficient coding and its implementation via (unsupervised) local learning rules, in the context of our ongoing work (e.g. [1-5]), we welcome your application. In our group, we combine approaches from statistical physics, data science, control theory, and information theory to carve out how local, unsupervised learning shapes network function. Potential collaborations with our colleagues from theory and experiment e.g. in Göttingen, Bonn, Tübingen, or Oxford are possible and encouraged.

Deadline: February 10, 2023 (first position) and April 30, 2023 (second position). It should not matter, which application deadline you choose.

Further Details:
https://www.viola-priesemann.de/open-positions-2/ or
https://www.nature.com/naturecareers/job/two-phd-mfx-or-postdoc-positions-mfx-on-computational-neuroscience-max-planck-institute-for-dynamics-and-selforganization-mpids-769309

Please note also our complementary open position on self-organization in social networks.

References:
[1] Zierenberg, Wilting & Priesemann, PRX 2018
[2] Mikulasch, Rudelt & Priesemann, PNAS 2021
[3] Jaehne et al., Cell Reports, 2021
[4] Rowland et al., biorxiv, 2021
[5] Mikulasch, Rudelt, Wibral & Priesemann, TINS 2022

-- 
Summer school on infodemics and pandemics: https://summerschool.infodemics.info/

Prof. Dr. Viola Priesemann
Max-Planck-Institute for Dynamics and Self-Organization &
Georg-August-University Göttingen
Am Fassberg 17, 37077 Göttingen
https://priesemann-group.github.io
www.viola-priesemann.de
viola.priesemann@ds.mpg.de