Title: Bio-inspired learning algorithms for efficient and robust neural network models with a focus on neuromorphic computing and control of complex environments

This project is embedded within the European Laboratory for Learning and Intelligent Systems (ELLIS) Unit in Radboud AI. The aim is to develop highly effective algorithms for training artificial neural network models which make use of (biologically plausible) information local to the individual nodes of the network. This locality will allow efficient implementation and testing of these algorithms on neuromorphic computing systems. The efficacy of the algorithms will be tested in simulations. This will be done in the context of relevant reinforcement learning tasks using a control system theoretic approach to interaction with modelled environments. This work entails a theoretical (mathematical) development of state-of-the-art biologically plausible learning rules and reinforcement learning strategies alongside implementation/testing of these algorithms in the form of neuromorphic computing algorithms and agent-based artificial neural network modelling.

For more information and to apply see:
https://www.ru.nl/english/working-at/vacature/details-vacature/?recid=1147553&pad=%2fenglish&doel=embed&taal=uk