Environmental intelligence

Our research is focused on:

Environmental data integration and predictive modelling. We develop advanced computational frameworks that integrate monitoring data, remote sensing, multi-omics, and environmental simulations. These tools support comprehensive predictions of environmental processes, ecosystem responses, and human exposure.

AI for environmental and hydrogeological processes. We advance AI-assisted and hybrid process-based models to improve the forecasting and representation of hydrological, biogeochemical, and environmental processes across multiple scales.

Explainable and trustworthy AI. We develop interpretable AI and machine-learning approaches to improve model transparency, robustness, and reproducibility. This provides reliable scientific evidence for environmental risk assessment and regulatory frameworks.

Digital Twins and smart environmental monitoring. We combine smart sensing technologies, real-time observations, and predictive analytics to build Digital Twins. These platforms enable early warning, scenario analysis, and proactive risk management for environmental hazards.

Environmental software and decision-support systems. We create interoperable software platforms and decision-support tools that translate complex monitoring, modelling and assessment data into actionable science for policy and sustainable environmental management.