The successful candidate will work on learning algorithms for spiking neural networks in the international consortium of the international project 'Scalable Learning Neuromorphics'. We will develop in this project learning algorithms for spiking neural networks for memristive hardware implementations. This project aims to develop scalable Spiking Neural Networks (SNNs) by leveraging the integration of 3D memristors, thereby overcoming limitations of conventional Artificial Neural Networks (ANNs). Positioned at the intersection of artificial intelligence and brain-inspired computing, the initiative focuses on innovative SNN training methods, optimizing recurrent connections, and designing dedicated hardware accelerators. These advancements will uniquely contribute to scalability and energy efficiency. The endeavor addresses key challenges in event-based processing and temporal coding, aiming for substantial performance gains in both software and hardware implementations of artificial intelligence systems. Expected research outputs include novel algorithms, optimization methods, and memristor-based hardware architectures, with broad applications and potential for technology transfer.