Human-Centric AI aims to develop AI systems that can interact and reason in ways that align with human behaviours, ultimately augmenting and helping humans in their decision-making processes. A key challenge in this domain is building AI agents capable of understanding and integrating within complex social interactions, by learning from multimodal behavioural data and adapting to dynamic environments.

Since human interactions and behaviours naturally form structured patterns—such as social networks, communication graphs, and relational data—Graph Neural Networks (GNNs) provide a powerful framework to model these structures. This PhD position will explore the intersection of human-centric AI and graph-based learning, investigating how GNNs can enhance AI-driven simulations of human behaviour and enable adaptive, context-aware AI systems (e.g., achieving human-ai complementarity).

The ideal candidate should have a strong background in machine learning, some experience in Human-Centric AI and/or Graph Neural Networks. The selected candidate could also be a PhD candidate within the ELLIS doctoral school. Contact person: Bruno Lepri ([email protected])