PhD Hydrological model-data interaction and machine learning headwater catchment analyses

Your position

This project will advance multi-fidelity modelling approaches tailored to headwater systems and deliver new insights into groundwater‑surface water interactions. A key challenge for hydrological headwater catchment analyses lies in characterizing and predicting their hydrological functioning, and this is fundamentally related to the fact that the majority of them are unmonitored and their hydraulic parameters unknown. The objective of this PhD project is to bridge this gap and develop data‑driven, machine learning‑based approaches to identify the governing hydraulic parameters of headwater catchments, hind‑ and forecast their stream and groundwater outflows, and to understand their susceptibility to extreme hydrometeorological conditions based on storyline approaches.

• Contribute to the development of the multi‑fidelity modelling platform Hydro Mod Py
• Implement machine‑learning based approaches to estimate hydraulic properties across the headwater catchments of Europe
• Evaluate hydrological validation approaches for headwater catchment properties identification
• Implement hybrid‑modelling approaches to hind‑ and forecast the hydrological behavior of ungauged headwater catchments using climate storylines

The outcomes of this project will support both the scientific community and practitioners by improving the assessment and prediction of hydrological responses to climate change.

• MSc in Hydrology, Hydrogeology, Data/Computer Science or a related field
• Strong interest in environmental data analysis and/or numerical modelling
• Proficiency in Python programming
• Fluency and excellent writing skills in English, with a strong interest in scientific and public communication