The postdoc position is focused on self-organized network modelling. The project aims to develop a theory of learning in liquid brains, focusing on how liquid brains learn and their adaptive potential when embodied as an agent interacting with a changing external environment. The goal is to extend the concept of liquid brains from a theoretical concept to a useful tool for the machine learning community. This could lead to more open-ended, self-improving systems, exploiting fluid reconfiguration of nodes as an adaptive dimension which is generally unexplored. This could also allow modes of learning that avoid catastrophic forgetting, as reconfigurations in the network are based on reversible movement patterns. This could have important implications for new paradigms like edge computing.