PhD candidate: Green&decentralized AI

Vedi l’offerta su Go Lavoro: golavoro.it/annuncio-lavoro/7c0sma

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 is a scouting notice to raise awareness: the University of Pisa has officially opened Ph.D. admissions, and applications are now being accepted.

If the topic sounds like something you'd enjoy working on, please send us your CV and academic transcripts combined into a single PDF (LinkedIn doesn't allow multiple file uploads) to get preliminary feedback on how your profile aligns with the topics funded by IIT-CNR in this call. Please note that, due to the number of applications expected, we'll be able to follow up only with candidates who are a strong fit. You're also welcome (and encouraged) to submit your application through the official call using the link below.

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?️ ????????: 10 August 2026, 13:00 CEST

? ??????? ?????: 1 November 2026

⏳ ????????: 3 years

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.

Vedi l’offerta su Go Lavoro: golavoro.it/annuncio-lavoro/7c0sma