The Medical University of Graz plays a central role in the RI-SCALE project and is developing new technologies for digital pathology and synthetic data. The aim is to make data-intensive research more efficient with AI-supported analyses. The project was launched in March with a kick-off meeting in Amsterdam.
Huge amounts of data are part of everyday life in modern science - but their full potential often remains untapped because there is no way to analyse them using artificial intelligence (AI). This is precisely where the EU project RI-SCALE comes in: It creates an infrastructure that brings scientific data quickly, securely and in a standardised way to where AI needs it - to the GPUs (Graphics Processing Units) of modern high-performance computers.
GPUs are the computing hearts of today's AI - they enable the simultaneous processing of large amounts of data and are therefore indispensable for machine learning, image analysis or language models. In order for these powerful processors to be used efficiently, an intelligent bridge is needed between data storage and the data centre. RI-SCALE builds this bridge.
The technology developed is currently being used in the environmental and life sciences - with leading partners such as BBMRI-ERIC (the European research network for biobanks), Euro-BioImaging and CERN. The Medical University of Graz also plays a central role in this: it is one of the pioneers in Europe in the field of digital pathology and contributes its expertise specifically to the development of AI-supported analyses of medical image data.
RI-SCALE integrates seamlessly with strategic European initiatives such as EOSC (European Open Science Cloud) and Gaia-X to promote a sovereign, trustworthy and powerful European data infrastructure.
With automated data provision, scalable AI tools and the highest safety standards, RI-SCALE enables a new quality of research work - efficient, reproducible and ready for the challenges of tomorrow.
RI-SCALE brings scientific data to where knowledge is generated - directly to the computing units of the future.
Project duration: 2025-2027
Funded by: European Union
Project partners: Stichting EGI, BBMRI-ERIC, BBMRI.at (Med Uni Graz), CERN, Masarykova Univerzita, TU Wien, fragmentiX, EMBL, MMCI, DKRZ, Universitat Politecnica de Valencia
