Max Planck Research Group Leader Neural Systems Theory 
- since 2022: Group Leader, MPI for Dynamics and Self-Organizaiton, and Professor, Department of Physics, Georg-August University Göttingen
 - since 2016: Max Planck Research Group Leader, MPI for Dynamics and Self-Organization, Göttingen, Germany
 - 01/2017 – 03/2017: Guest Researcher, Ernst-Strüngmann-Institute, Frankfurt
 - 2014 – 2016: Bernstein Fellow and Group Leader, Bernstein Center for Computational Neuroscience & MPI for Dynamics and Self-Organization, Göttingen, Germany
 - 2013 – 2014: PostDoc, MPI for Dynamics and Self-Organization, Göttingen, Germany
 - 2013: PhD, Goethe University Frankfurt, Germany
 - 2008 – 2013: Research Projects at the Ecole Normale Superieure (Paris, France), Caltech
 - Pasadena, USA), MPI for Brain Research & FIAS (Frankfurt, Germany)
 
Honours, Grants & Service to the Community
- since 2023: member of the Göttingen Academy of Science and Humanities
 - 2022: Lise Meitner Lecturer, German Physical Society (DPG) and Austrian Physical Society (ÖPG)
 - 2022: Arthur Burkhardt Award
 - 2021: Dannie Heineman Award of the Göttingen Academy of Sciences and Humanities
 - 2021: Lower Saxony Science Prize
 - 2021:Communitas Prize, Max Planck Society
 - since 2021: member of Junge Akademie
 - since 2020: Associated to the Max Planck - U of Toronto Centre of Neurophysics
 - since 2020: Lead-PI in a project of the SPP 2205 “Evolutionary Optimisation of Neuronal Processing”
 - since 2019: Scientific Member of the Max Planck Society
 - since 2019: Initiation of the “Göttingen Neural Networking” Days
 - 2016 – 2017: German-Israel Foundation (GIF) Young Investigator Grant
 - since 2015: Fellow of the Schiemann Kolleg of the Max Planck Society
 - 2014 – 2016: Bernstein Fellow
 - 2010: Thomas B. Grave and Elizabeth F. Grave Scholarship
 
Major Research Interests
- Neural Networks
 - Neural Networks
 - Information Processing
 - Statistical Physics
 - Nonlinear Dynamics
 - Collective Phenomena
 - Living Computation
 - Self-Organization of Computation
 - Neural Plasticity & Learning
 - Homeostatic Plasticity
 - Design and Optimization of Neural Computation
 - Information Theory