FutureData4EU

Training Future Big Data Experts for Europe

Abstract

Big-data management through high-performance computing is nowadays considered as the richness of the new knowledge economy, providing unprecedented opportunities in several R&I; and applicative domains, from climate change to health, from mobility to cultural heritage, from digital change in production systems to new materials and financial analysis. In Italy, 70% of the national computing and storage capacity is set in the Emilia-Romagna Region, making it an authentic Data Valley. While supercomputing infrastructures are a pre-requisite for European competitiveness, data-driven R&I; needs qualified experts in datascience and big-data application in relevant societal/industrial domains. In this context, the University of Bologna in partnership with the other 5 universities located in the region, with the support of the Regional Authority and involving the relevant stakeholders, is proposing FutureData4EU, to train the data-science experts of tomorrow through an interdisciplinary, international and cross-sectoral approach. FutureData4EU builds on existing PhD courses will offer a pioneering combination of training in research and transversal skills in the multi- and trans-disciplinary field of Big-Data, through the enabling technologies and the Thematic Areas representing the Horizon Europe Clusters (1. Health; 2. Culture, Creativity and Inclusive Society; 3. Civil Security for Society; 4. Digital, Industry and Space; 5. Climate, Energy and Mobility; 6. Food, Bioeconomy, Natural Resources, Agriculture and Environment).

Project details

Unibo Team Leader: Alberto Credi

Unibo involved Department/s:
Area Ricerca

Coordinator:
ALMA MATER STUDIORUM - Università di Bologna(Italy)

Total Eu Contribution: Euro (EUR) 5.342.403,00
Project Duration in months: 60
Start Date: 01/11/2023
End Date: 31/10/2028

Cordis webpage
Project website

This project has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No 101126733 This project has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No 101126733