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EMBO | EMBL Symposium: AI and biology

Event category: EMBL

Date: March 10, 2026 to March 13, 2026

Event website: https://www.embl.org/about/info/course-and-conference-office/events/EES26-02/

Location: EMBL Heidelberg and Virtual, EMBL Advanced Training Centre

Organizers:

Mohammed AlQuraishi, Columbia University, USA
Wolfgang Huber, EMBL Heidelberg, Germany
Anna Kreshuk, EMBL Heidelberg, Germany
Mor Nitzan, The Hebrew University of Jerusalem, Israel

 

Event description

Symposium overview

Artificial intelligence (AI) is transforming virtually all areas of science, and the life sciences are no exception. Following the success of the inaugural 2024 meeting – which brought together an engaged community of researchers both in person and online – the 2026 edition marks the second installment of this growing scientific gathering.

AI-based methods are pushing the limits of what is possible in experimental design, data analysis, simulation, and modelling. The aim of this symposium is to catalyse synergistic interactions between AI researchers in different subfields of biology and biomedicine by exploring shared theoretical approaches, cross-domain experiments, foundation models, and data integration, as well as shared topics in dissemination of tools, agent-based workflows and collaboration with experimental labs. Furthermore, we will look into the future and discuss how the concerted actions of AI and life science communities can enable both fields to exploit the full potential of the ongoing data deluge and find new application areas ripe for disruption by AI.

AI-based analytics is subject to active research in at least three major application areas: sequencing, imaging and structural biology. While all three domains are experiencing very fast growth, the developments are largely happening independently. The aim of our symposium is to convene these communities to discuss topics of mutual interest to make progress, including on the theoretical/methodological side and on the more strategic issues of identifying the next areas of high potential.

 

Session topics

  • Foundation models for biology
  • Cross-domain, cross-modality learning
  • Explainable AI and mechanistic interpretability
  • From algorithms to biology
  • Generative modeling

Emerging topics

#EESAIBio

EMBL

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