Workshops and courses

ELIXIR node event Artificial Intelligence and Machine Learning in Life Sciences: from foundations to applications 2026

AI & ML in LS 2026

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Date: 14 - 18 September 2026

Timezone: Brussels

Language of instruction: English

Artificial Intelligence (AI) and Machine Learning (ML) are rapidly transforming life sciences, enabling new approaches to the analysis, interpretation, and integration of increasingly large and complex biological datasets. To fully benefit from these advances, researchers need both a solid understanding of AI/ML methods and awareness of their limitations, best practices, and regulatory frameworks.

This five-day, hands-on training course will guide participants from the foundations of machine learning through deep learning, foundation models, and generative AI, while also covering reproducibility, the DOME recommendations, and the EU AI Act.

Building on the success of the first edition, this second ELIXIR Summer School on Artificial Intelligence and Machine Learning in Life Sciences will bring together AI/ML experts from five ELIXIR Nodes and 25 participants from across the ELIXIR community, fostering knowledge exchange, collaboration, and the responsible adoption of AI in life sciences.

Keywords: AI, ML, Generative AI, FAIR principles

Venue: CRG – Centre for Genomic Regulation

City: Barcelona

Region: Barcelona (ca)

Country: Spain

Postcode: 08003

Prerequisites:

The level of this course is intermediate, with the following requirements:

  • Intermediate Python programming
  • Experience with data analysis and statistical reasoning
  • Experience with Jupyter Notebooks is desirable
  • Some prior knowledge of machine learning is a plus

During the registration process you will be asked to take a short quiz to assess your level and to help us get to know you better.

Learning objectives:

  • Apply the general machine learning data analysis pipeline to tabular data.
  • Implement and train deep neural networks to solve tasks such as image and sequence classification.
  • Use unsupervised approaches to analyse genomics datasets.
  • Evaluate probabilistic modelling approaches for their own data and research questions.
  • Describe techniques for fine-tuning pre-trained large language models.

Organizer: CRG, ELIXIR

Host institutions: CRG

Eligibility:

  • Registration of interest

Event types:

  • Workshops and courses

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