Project

RenAI (HORIZON-INFRA-2025-01-EOSC-05-101291378) will drive a new paradigm in the use of Generative AI for science by delivering a suite of domain-specific and cross-domain foundation models, co-created with European research infrastructures and scientific communities. These models, trained on curated multimodal datasets—including imaging, time series, and tabular data—will support a wide range of downstream applications, from environmental monitoring to health research, while ensuring reproducibility, robustness, and scientific credibility.
The project create a federated ecosystem, aligned with EOSC federation strategy, where researchers can easily access, fine-tune, and apply these models, supported by tools for data discovery, transformation, benchmarking, and compliance. Synthetic data generation will further enhance research capacity by enabling experimentation in data-scarce or privacy-sensitive domains. To ensure usability, RenAI will provide the coResearcher, an intuitive LLM-based assistant that lowers technical barriers and enables seamless interaction with models, services, and workflows.
RenAI builds on and extends initiatives such as AI4EOSC, iMagine, and EOSC Data Commons, embedding its services into the EOSC federation to guarantee interoperability, openness, and long-term sustainability. The project also establishes strong links with EOSC governance and EU policy frameworks, aligning its outputs with the AI Act, FAIR principles, open science requirements, and security and ethics standards.
Beyond technical innovation, RenAI emphasises uptake and impact through training programmes, community engagement, and cross-disciplinary collaboration, ensuring that infrastructures and researchers alike benefit from its outputs. By integrating ethical, transparent, and trustworthy AI into scientific practice, RenAI positions Europe at the forefront of applying GenAI to accelerate discovery and address societal challenges across domains.