Who we are and What we do

We are a research group. We do AI for Science, focusing on building machine intelligence that works alongside human reasoning to accelerate scientific discovery. Our core conviction is that AI systems must go beyond pattern recognition: they need to reason, explain, and collaborate with humans in the pursuit of scientific knowledge.

Research Themes

Neurosymbolic AI: We integrate deep neural networks with symbolic methods such as Inductive Logic Programming to build models that are accurate, interpretable, and data-efficient.

Deep Learning: We work on sequence models, transformers, LLMs, uncertainty quantification, and explainability, with a focus on reliability for scientific applications.

Computational Biology and Drug Discovery: We apply AI to gene regulation, genomic foundation models, multi-omics cancer analysis, and early-stage drug design.

Causality: We develop causal machine learning methods that go beyond correlation to discover causal structure in biological and clinical data.

Read more about our research →

News

• Apr 2026: We are hiring! We are looking for motivated PhD students, postdocs (with own fellowship), and thesis/project students interested in neurosymbolic AI and AI for science. See opportunities