A fully funded 3-year Ph.D. position in Green, continual, and Decentralized AI within the National PhD AI Program at the University of Pisa (Italy) is sponsored by the Ubiquitous Internet (UI) research group of IIT-CNR . We are looking for a highly motivated Ph.D. candidate with a strong academic background to join our research team and work on this Ph.D. topic under our supervision. This scouting notice aims to raise awareness of the upcoming Ph.D. admission call that will be officially opened by the University of Pisa in June. We are currently collecting expressions of interest. Research Topic Modern AI systems face some increasingly pressing challenges: their environmental footprint, their difficulty adapting to new data without forgetting old knowledge, and their reliance on centralized infrastructures that raise concerns around privacy and resilience. This PhD position is broadly centered around these themes, which can be tackled individually or in combination depending on the candidate's background and interests. Green AI is concerned with reducing the computational and energy cost of machine learning — from training large models to deploying them efficiently on resource-constrained devices. As AI systems grow in scale, understanding and minimizing their environmental impact becomes an important research problem in its own right. This topic includes questions such as how to design models that achieve strong performance with fewer parameters, how to lower the energy consumed during training and inference, to mention a few. Continual and decentralized learning studies how models can adapt to new data over time without forgetting what they have previously learned, in settings where this learning happens across networks of devices without a central coordinator. Most current systems are trained once on a fixed dataset and rely on centralized architectures; making them work in non-stationary, open-ended, and distributed environments raises questions around privacy, communication efficiency, robustness, and knowledge retention — especially when the network itself is dynamic, with devices and connections appearing and disappearing over time. These two directions naturally interact but can also be pursued independently, and the research focus will be defined together with the selected candidate. Ideal Candidate Profile: MSc in Computer Science, Mathematics, Physics, or a related field Background or interest in machine learning, optimization, or distributed systems Comfortable with Python and at least one deep learning framework Who Should Apply? This is a PhD position. The work can lean more algorithmic, focusing on designing learning methods, or more theoretical, studying the underlying dynamics and guarantees. If this sounds like something you'd enjoy working on, feel free to get in touch.