A refresh of our Strategic Agenda

The European Union’s visions for a healthier, wealthier, more sustainable society are intertwined with its ambitions for artificial intelligence (AI). The ELISE network is building a powerhouse of European AI that can deliver these goals.  This refresh of our Strategic Research Agenda shows how sustained investment can ensure the long-term success of this innovation ecosystem.

A European AI powerhouse:

Building an ecosystem of excellence and trust

The European Union’s visions of a healthier, wealthier, more sustainable society are intertwined with its ambitions for artificial intelligence (AI). Those ambitions seek to achieve European excellence in this strategically important technology area, to generate economic and societal benefits through AI adoption across sectors, and to create an ecosystem of trust that aligns AI progress with public interests. The ELISE network is building a powerhouse of European AI that can deliver these goals.

By accelerating the technical capabilities of AI technologies, improving their performance in deployment, and aligning AI development with societal needs, the first phase of ELISE’s work has already nurtured a thriving research and innovation community. Building on these successes, this refresh of its Strategic Research Agenda shows how sustained investment can ensure the long-term success of this innovation ecosystem.

An evolving research-policy agenda

ELISE’s Strategic Research Agenda and trends in AI

ELISE’s 2021 Strategic Research Agenda set out the research challenges that needed to be addressed to strengthen the technical capabilities of AI; improve its performance in deployment; and align AI development with societal interests.

This Agenda sought to bridge between the frontiers of technology development and the EU’s AI policy agendas, recognising that the success of those policy agendas would depend on Europe’s ability to pursue excellent research that both advances foundational AI technologies and applies those technologies to areas of critical social and scientific need.

ELISE AI roadmap

ELISE is driving a new wave of research and development to deliver AI ‘made in Europe’. Together, its 14 research programmes create AI methods, techniques, and toolkits that are technically innovative, safe, and effective in deployment, while being aligned with social needs. By combining our research agenda with initiatives to attract top talent to Europe, train the next generation of AI researchers, and enhance local start-up and innovation networks, ELISE is creating a European AI ecosystem of excellence and trust.

AI technologies that are technically advanced

Methods and tools to analyse real-world, multi-modal data; Strengthen core machine learning capabilities, through methodological and theoretical advances, such as techniques to bridge between data-driven and domain knowledge; Interrogate workings of complex systems through advances in simulation, emulation, and causality.

AI technologies that are robust in deployment

AI that is robust under dynamic or uncertain conditions; Human-centric tools that are effective as decision-support; Methods to enhance explainability in decision-making.

AI technologies that align with societal interests

Techniques for trustworthy AI; Deployed AI that is integrated into areas of critical need; AI research and development that engages stakeholder perspectives.

  • Advance the science of artificial intelligence by better understanding the intelligent behaviour of living systems and how this emerges.
  • Strengthen the theoretical underpinnings and algorithmic capabilities of machine learning, creating more reliable, efficient and usable machine learning systems.
  • Design new, energy-efficient machine learning algorithms and hardware implementations, drawing from concepts in quantum physics and statistical physics to develop more powerful machine learning systems.
  • Build bridges between classical AI methods and machine learning to advance further progress in computer vision.
  • Explore the role of causal modelling as a bridge between observational and interventional learning, identifying the principles for interactive learning systems.
  • Push forward the foundations of multimodal learning systems and expand their application.
  • Improve the performance of deep learning systems.
  • Understand the principles for robustness in deployment and develop techniques for machine learning that reliably performs well.
  • Build systems for general-purpose natural language understanding and generation.
  • Improve core machine learning functions, for example through enhanced methods for deep learning, computer vision, natural language understanding and generation, and semantic, symbolic, and interpretable machine learning.
  • Create robotic systems that can interact intelligently with the world around them by combining robot learning approaches with machine learning methods, such as reinforcement learning; and information systems that can better understand human behaviour.
  • Create AI systems to support the delivery of effective public services, for example creating AI systems for healthcare that can monitor patient health, using complex datasets to develop decision-support systems and to foster breakthrough applications in healthcare and biomedicine.
  • Develop AI tools that can contribute to humanity’s response to the climate crisis, increasing understanding of climate extremes, changes to earth systems and potential areas for intervention.
  • Design novel machine learning algorithms that are better aligned with human needs and societal interests, for example taking into account concerns around fairness, privacy, accountability, transparency and autonomy.


ELLIS Fellows & ELLIS scholars


PhD students exchanged


PhD placements


registered advising faculty


incubator partners


total industrial and SME collaborators

Research Programs

Theory, Algorithms and Computations of Modern Learning Systems

Semantic, Symbolic and Interpretable Machine Learning

Robust Machine Learning

Quantum and Physics-Based Machine Learning

Multimodal Learning Systems

Machine Learning for Earth and Climate Sciences

Machine Learning and Computer Vision

Robot Learning: Closing the Reality Gap!

Interactive Learning and Interventional Representations

Machine Learning for Health

Natural Language Processing 

Natural Intelligence

Human-centric Machine Learning

Geometric Deep Learning


Industrial Collaborators

Kepler Vision

SME Collaborators

AEIGEA medical
Fuvex Civil
Mimica Automation:
Rovjok Oy
Skinive Holding
Stratio Automotive
Synamic  Technologies
Yield Systems
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