We are looking for PhD students which are interested in exploring causality aspects of Deep learning and Artificial Intelligence. This is a very trending topic in the Computer Science and Deep Learning community and impacts several research areas: explainable AI, recommender systems, formal logic, generative AI, automated learning, etc. Examples of aspects to be investigated are:

• Deep Learning and predictive methods applied to temporal data
• Causality aspects of learning policies and their evaluation
• Knowledge Representation and Linear Time Temporal Logic

This topic is particularly interesting because it can benefit from expertises coming from different disciplines: computer science, physics, mathematics, neurobiology. Therefore students with a strong scientific background in one (or more) of these subjects are encouraged to apply. Moreover, the research performed through this scholarship can range from abstract and foundational research to very applied one and therefore the topic can adapt to the candidate’s natural inclination and preferences.