PRIN-PNRR Projects

The National Recovery and Resilience Plan (PNRR) “Italia Domani” is a vast program of reforms – including public administration, justice, simplification of legislation, competition, and taxation – accompanied by adequate investments.

The PNRR is part of the Next Generation EU (NGEU) programme, often also referred to as the “Recovery Fund”, a 750 billion euro fund agreed upon by the European Union in response to the pandemic crisis. For the first time, joint European debt will finance a post-pandemic recovery program for EU countries. The main component of the NGEU program is the Recovery and Resilience Facility (RRF), which lasts six years, from 2021 to 2026, and a total size of 672.5 billion euros.

With the “PRIN 2022 PNRR” call of 14 September 2022 (D.D. 1409/2022), the MUR finances public research projects concerning one of the emerging strategic themes related to the objectives of a cluster of the European Framework Program for Research and Innovation 2021 -2027. The call aims to promote the national research system, strengthen interactions between universities and research centres in line with the objectives outlined by the PNRR, and encourage Italian participation in initiatives relating to the Union’s Framework Program for Research and Innovation in Europe.

MULTIPASS – MULTIPle trAcker for Secondary particleS monitoring

Codice Progetto: P2022FZAC3

Coordinatore: INFN Milano

Referente CREF: Michela Marafini

This project aims to develop a new technology for detecting secondary radiation of different types (prompt photons, protons, and neutrons) in the framework of particle therapy. This multipurpose tracker is a compact detector made of plastic scintillating fibers, read by integrated electronics, designed to meet geometry and timing constraints.

Triple T – Tackling a just Twin Transition: a Complexity Approach to the Geography of Capabilities, Labour Markets and Inequalities

Coordinatore: Angelica Sbardella (CREF)

Partner Scuola Superiore di Studi Avanzati Sant’Anna di Pisa

Codice progetto:  P2022B5S5J

Triple T presents an ambitious interdisciplinary research agenda that plans to identify the capabilities and the policy strategies needed to lead economies towards a just twin transition – i.e. the combination and mutual reinforcement of the digital and sustainable transition. Triple T aims to obtain far-reaching advances in understanding the asymmetries across sectors, countries and regions that the transition may entail in knowledge generation, labour markets, and environmental and socio-economic inequalities. The multifaceted nature of such societal transformation, wherein geographical, structural and institutional elements interact, calls for a complexity perspective suitable to analyse a scenario characterised by interlinkages and trade-offs.

CODE – Coupling Opinion Dynamics with Epidemics

Coordinator Stefano Guarino CNR

Referente CREF: Fabio Saracco

Partner Marco Brambilla, Politecnico di Milano

Codice Progetto: P2022AKRZ9


The CODE (Coupling Opinion Dynamics with Epidemics) project aims to investigate the coupling between virtual interactions, leading to opinion formation, and physical interactions, which cause the spreading of epidemics. We will implement an efficient and scalable open-source tool to simulate the spread of infectious diseases in a large-scale geo-localized population and generate alternative counterfactual scenarios. The tool will be made available to policy-makers and stakeholders interested in evaluating the combined impact of mitigation interventions and (dis)information campaigns.

REal-time motion CorrEctioN in magneTic REsonance 

Principal Investigator: Sapienza Università di Roma

Partner CREF : Federico Giove

Partner: CNR, INFN, Università Federico II

Codice Progetto: P202294JHK


The RECENTRE project proposes a novel high-speed, real-time prospective motion correction technique for MR spectroscopy of the human brain. The technique is based on modern Deep Learning methods, which provide a potential avenue for dramatically reducing the computation time and improving the convergence of retrospective motion correction while overcoming the limits of the current state-of-the-art prospective methods that are incapable of coping with complex motion patterns.