Europe's AI-in-Science Strategy Launches RAISE, €600m Gigafactories, and New Talent Networks

Europe puts AI at the core of research, backing labs with compute, funding, and skills to speed discovery while keeping trust. Prepare projects for Horizon calls and RAISE.

Categorized in: AI News Science and Research
Published on: Oct 09, 2025
Europe's AI-in-Science Strategy Launches RAISE, €600m Gigafactories, and New Talent Networks

Europe's AI in Science Strategy: What It Means for Your Lab

Europe is putting AI at the center of its research engine. A new strategy anchors funding, compute, and talent so labs can test ideas faster, process larger datasets, and open new lines of inquiry with confidence.

The aim is clear: lead in AI-driven discovery while keeping scientific sovereignty and public trust intact.

Core Commitments

  • RAISE (Resource for AI Science in Europe): A virtual institute to connect researchers with compute, data, and funding across Europe.
  • Talent: €58 million for new Doctoral and Excellence Networks to train and attract top researchers.
  • Compute at scale: €600 million via Horizon Europe for AI Gigafactories to secure dedicated computational power for scientists and startups.
  • Momentum: The current proposal aims to double support for AI in science by 2028.
  • Principles: Open, collaborative, interdisciplinary, and responsible-AI serves science, and science serves society.

Why This Matters

AI cuts time-to-insight from months to minutes and surfaces patterns that conventional methods miss. This strategy moves Europe from scattered pilots to a connected pipeline of resources that can sustain serious research at scale.

The result: faster progress on tough problems, stronger collaboration, and predictable access to compute and skills.

What To Do Next

  • Identify projects that would benefit from dedicated compute. Outline data needs, model types, and expected impact.
  • Prepare for Horizon Europe calls. Build consortia that meet open and responsible AI standards from day one.
  • Make reproducibility default: versioned datasets, documented training runs, model cards, and human oversight for critical decisions.
  • Form cross-disciplinary teams with domain scientists, ML engineers, and data stewards to shorten iteration cycles.
  • Audit data for FAIR readiness so your lab can plug into RAISE once operational.

What To Watch

  • RAISE governance, data-access policies, and how compute credits are allocated across institutions.
  • Timelines for AI Gigafactory capacity to reach universities and startups.
  • Eligibility and evaluation criteria for the Doctoral and Excellence Networks.

Useful Links

Europe is choosing to lead. The invitation to labs is direct: use AI to push the frontiers of knowledge, do it responsibly, and share the gains with society.