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ÖAW AI Winter School 2026

16–20 Feb 2026
Campus Akademie, Bäckerstraße 13, 1010 Wien
Europe/Zurich timezone

Contribution List

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  1. 1. Welcome
    Dr Claudius Krause (MBI Vienna (ÖAW)), Claus Trost (Erich Schmid Institute of Materials Science of theAustrian Academy of Sciences), Jan Odstrčilík (Institut für Mittelalterforschung, ÖAW), Kati Heinrich (IGF | ÖAW), Nicki Holighaus (Acoustics Research Institute, Austrian Academy of Sciences)
    16/02/2026, 12:00
  2. 3. What is AI? Overview lecture: Part I
    Michal Hradiš (Brno University of Technology)
    16/02/2026, 12:30

    1) This lecture introduces what “AI” means today and how it relates to machine learning, with an emphasis on large language models (LLMs) and AI agents. We’ll build intuition for core ML ideas—supervised vs. self-supervised learning, loss functions, optimization, and generalization.

    regression, classification, generation, language models, etc.

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  3. 2. AI and ML at the Austrian Academy of Sciences
    Dr Claudius Krause (MBI Vienna (ÖAW))
    16/02/2026, 14:30
  4. 4. Overview lecture: Part II
    Michal Hradiš (Brno University of Technology)
    16/02/2026, 16:00

    2) This lecture explains how today’s language models work at a high level: key building blocks, typical training stages, and the resulting capabilities and limitations. We’ll finish with a brief Python demo using the OpenAI API and a local model via Ollama.

    LLMs and LLM architecture, transformers, supervised and unsupervised training in more detail

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  5. 5. Vibe Coding & „Promptotyping”: Part I
    Christopher Pollin (DH Craft Graz)
    17/02/2026, 09:00
  6. 6. Vibe Coding & „Promptotyping”: Part II
    Christopher Pollin (DH Craft Graz)
    17/02/2026, 11:00
  7. 8. Vibe Coding & „Promptotyping”: Part II
    Christopher Pollin (DH Craft Graz)
    17/02/2026, 14:00
  8. 7. Vibe Coding & „Promptotyping”: Part III
    Christopher Pollin (DH Craft Graz)
    17/02/2026, 16:00
  9. 13. Bias in Machine Learning
    Philip Winter (VRVis)
    18/02/2026, 09:30
  10. 10. OpenEuroLLM and other developments on AI infrastructures
    Patrick Rarivoson
    18/02/2026, 11:30
  11. 9. AI Strategy at the Austrian Academy of Sciences (ÖAW)
    Bernd Wachmann (ÖAW)
    18/02/2026, 14:00
  12. 12. Governing Machines: From Moderation to Alignment
    Charlotte Spencer-Smith (CMC)
    18/02/2026, 14:45
  13. 11. AI Policy at the Vienna University
    Laura Gandlgruber (Vienna University)
    18/02/2026, 16:15
  14. 15. From idea to final project: Part I - Data & Code Management
    Stavrina Dimosthenous (University of Manchester)
    19/02/2026, 09:00

    Delving into the world of data-driven research presents exciting possibilities. However, there are a few topics that need to be addressed before one can jump right in.
    Research data management (RDM) planning, software version control, automation, and data cleaning might appear dry at first glance, and they might indeed be to many. Despite these topics and practices seeming like ‘checkbox’...

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  15. 21. From idea to final project: Part II - Data & Code Management
    Stavrina Dimosthenous (University of Manchester)
    19/02/2026, 11:30

    Delving into the world of data-driven research presents exciting possibilities. However, there are a few topics that need to be addressed before one can jump right in.
    Research data management (RDM) planning, software version control, automation, and data cleaning might appear dry at first glance, and they might indeed be to many. Despite these topics and practices seeming like ‘checkbox’...

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  16. 16. From idea to final project: Part III - Data cleaning, reproducability
    Stavrina Dimosthenous (University of Manchester)
    19/02/2026, 14:00

    Delving into the world of data-driven research presents exciting possibilities. However, there are a few topics that need to be addressed before one can jump right in.
    Research data management (RDM) planning, software version control, automation, and data cleaning might appear dry at first glance, and they might indeed be to many. Despite these topics and practices seeming like ‘checkbox’...

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  17. 17. Introduction to CLIP
    Erich Birngruber (CLIP)
    19/02/2026, 16:00
  18. 18. Explainability of Neural Networks in STEM fields
    Gunnar König
    20/02/2026, 09:00

    Due to their predictive accuracy, machine learning models are
    increasingly employed in high-stakes decision making and in scientific
    contexts. These predictive capabilities often come at the price of
    interpretability: The models are too complex to be inherently
    intelligible by their human users.
    In an attempt to restore this interpretability, a broad range of methods
    have been developed,...

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  19. 19. Explainability of Neural Networks in STEM fields
    Moritz Grosse-Wentrup
    20/02/2026, 11:30

    Machine learning (ML) models are increasingly deployed in high-stakes
    environments, e.g., in the health domain, where ethical and legal
    considerations require models to be interpretable. Despite substantial
    progress in interpretable ML (IML), several key challenges remain. These
    include distinguishing between interpreting the model and using the
    model to interpret the data-generating...

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  20. 20. Evaluation and Interpretability of NLP systems
    Pia Sommerauer (Vrije Universiteit Amsterdam)
    20/02/2026, 13:30
  21. 14. Dinner at Stöckl im Park