We seek a candidate for a PhD position in neuro-symbolic AI, with expertise in both neural networks and symbolic reasoning techniques. Neuro-symbolic AI refers to hybrid approaches to artificial intelligence that combine machine learning (and in particular deep neural networks) with symbolic reasoning techniques. These approaches aim to leverage the strengths of both paradigms: neural networks excel at learning patterns and extracting features from large datasets, while symbolic systems can provide abstract interpretable representations and logic-based or algorithmic reasoning. The candidate will have to study and develop neuro-symbolic systems that address some limitations in current AI systems, such as the difficulty in generalizing knowledge across different domains or tasks. This may involve designing novel architectures that integrate neural and symbolic components, developing algorithms for learning and reasoning with hybrid representations, and exploring new applications where neuro-symbolic approaches can offer some advantages over purely neural or symbolic methods.