Data science and engineering focuses on designing scalable, reliable, and sustainable methods for acquiring, integrating, and transforming heterogeneous data into actionable analytical and AI-ready knowledge.

This area examines model-driven abstractions and federated architectures that support secure, interoperable, and regulation-compliant data sharing across organizational boundaries like data spaces in Gaia-X and IDS).

This area focuses on developing sustainable, open, and reproducible research software that enables robust data pipelines, analytics-aware models, and long-term maintainability in data-intensive scientific workflows.