Collaborative/integrative research programs (IP).

ANITI’s research and technology transfer roadmap is also structured around so-called integrative programs, designed to develop trustworthy AI—that is, AI that is effective, safe, and ethical. These programs are central to our strategy for addressing the multidisciplinary challenges of fundamental research, development, and the integration of artificial intelligence into ANITI’s key application sectors: Transport, Industry of the Future, and the Environment.

They serve as focal points for interaction among ANITI’s Chairs and our broader academic and industrial ecosystem. Over time, the programs will be enriched by a variety of projects, ranging from fundamental research to demonstrators, in line with our long-term goals and in response to new opportunities and scientific progress.

ANITI is committed to allocating dedicated resources to leverage additional funding—whether private, public, or European—to support these projects. We also actively encourage the development of educational modules, especially those combining AI with other disciplines (AI + X).

Each integrative program fosters close collaboration among academic staff (Research Chairs, , researchers, faculty) and industry experts working within ANITI to enable seamless and sustained interaction.

Current integrative programs:

• DEEL: Certifiable AI for Critical Embedded Systems (Approved)
• FOR: Trustworthy AI / GenAI for Critical Processes
• PhysimAI: AI for Simulation and Virtual Sensors
• IndustryXAI:
  • Adaptive cobots with natural interaction
  • Trustworthy AI for planning and supply chain optimization
  • Prognostics and Health Management (PHM) and anomaly detection
• AI4ENV:
  • Earth observation and digital twins
  • AI for climate modeling and forecasting
  • Decision-making to address climate change
• AI4Decarbo:
  • Sustainable AI for air traffic management (CO₂ / non-CO₂ trajectories)
  • Digital twins for more electric vehicles
  • Robust optimization for power systems
• ADoptAI:
  • Social acceptance of AI
  • Legal and regulatory aspects
  • Neuroergonomic approaches to improve human–AI interaction

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