saFe learNIng for lArge scaLe InTerconnected sYstems

FINALITY evolves the theoretical computer science curriculum focusing on the mastery of prompt and safe learning techniques for interconnected systems.

The trainee team will develop and integrate innovative methodological tools specialized for AI-intensive resource allocation, particularly in the context of large-scale critical infrastructures for communication and computing.

They will combine AI methods that are safe by respecting system boundaries and are prompt in adapting to the environmental changes.

Throughout their research training, the FINALITY candidates will prioritize the principles of fairness and computational parsimony of AI methods.

The FINALITY doctoral team will be supported by a world-class team of academic and industrial advisors, who work routinely on all the tools used in AI-based RA, advancing their theoretical foundations and their application in the industrial domain.

They possess extensive experience in training doctoral students, and an excellent track record of joint research activities across the consortium. International exposure and dissemination are ensured by an extra-EU supervisory board