Apart from a scientific semester programme CWI organizes mini-symposia, masterclasses, lectures with renowned speakers, hackathons and data challenges. Additionally, we organize and host other regular scientific meetings like reading groups, seminars, collaboration sessions and open-problem sessions. The format is flexible to meet the preferences of the participants.
Events
Find future and past events.
Event types that you can find in this section:
ML Seminar Bojian Yin (Institute of Automation, Chinese Academy of Sciences, Beijing)
Selective-Update RNNs: A New Architecture for Long-Range Sequence Modeling
3rd ERCOFTAC Workshop Machine Learning for Fluid Dynamics
PhD Defence Xuemei Zhou (Distributed and Interactive Systems)
The PhD defence of Xuemei Zhou will be on Wednesday 4 March 2026 at Delft University of Technology on "Human-Centric Quality Assessment and Visual Attention Modeling for Point Clouds"
ML Seminar Andrew Nobel (University of North Carolina at Chapel Hill)
Graph Joinings, Graph Isomorphism, and Reversible Markov Chains
Scientific Computing Seminar Daniele Avitabile (VU)
Programme Probability Problem-Solving Workshop 30 March - 2 April 2026
How can we understand the underlying structure of a large-scale network? What local constraints impact the running time of an algorithm? In recent years physics intuition has become fruitful in tackling such questions. Key to this is the notion of a phase transition, that is, a drastic change in macroscopic behaviour (e.g. matter changing from frozen to liquid at some critical temperature) in models governed by local interactions. We aim to connect different communities –combinatorics, algorithms and probability– through the lens of such transitions. This will be achieved through three interactive 4-day workshops focused on problem-solving and collaboration.
Open Lectures Spring School on Social XR 2026
The Distributed and Interactive Systems research group (DIS) of CWI presents the open lectures of the 4th edition of the Spring School on Social XR.
PhD defence Syver Agdestein (Scientific Computing)
Data-driven discrete closure models for large-eddy simulation of incompressible turbulence
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