PhD Positions in Flood Forecasting, Hydrology, and Machine Learning at Wageningen University Wageningen University & Research in Netherlands

Wageningen University & Research (WUR) is offering two fully funded PhD positions focused on flood forecasting, hydrology, and machine learning. These positions are part of the NWO-funded VIDI project “FutureFlood - Future-proof flood prediction,” aiming to develop advanced models and classification frameworks for predicting and understanding extreme flood events and their generating processes.

The first PhD project centers on the incorporation of flood types in tailored rainfall-runoff models. The candidate will compare hydrological models (such as HBV, WALRUS, and LISFLOOD) for global case studies, develop new model versions tailored to specific flood types, and analyze robustness and sensitivity. The research will also involve testing models under climate change scenarios and applying them to estimate design flood events. The second PhD project focuses on ML-based prediction of flood types using atmospheric and catchment attributes. The candidate will test various machine learning algorithms (ANNs, WNNs, SVM, CART, and possibly fuzzy-based algorithms) to predict flood types and analyze their performance across diverse climate zones and catchments worldwide.

Both positions are based in the Hydrology and Environmental Hydraulics Group at WUR, a dynamic team engaged in cutting-edge research on hydrological processes, environmental hydraulics, and the development of novel sensing and modelling techniques. Supervision will be provided by Svenja Fischer, Ryan Teuling, Josh Ho, and Claudia Brauer. The research environment is international, inclusive, and supportive, with a strong focus on collaboration and innovation.

Eligibility: Applicants must hold a completed MSc degree in hydrology, earth systems, water management, statistics, or a related field. Advanced programming skills (preferably in R or Python), experience with hydrological modelling or data analysis, and proficiency in English (C1 level) are required. Knowledge of statistics, clustering, pattern analysis, and machine learning is advantageous. Candidates should be collaborative, creative, and able to communicate with diverse partners.

Funding: The positions are fully funded for up to four years, with a gross salary starting at €3,059 per month and rising to €3,881 in the fourth year (full-time, 38 hours/week). Benefits include partially paid parental leave, year-end bonus, excellent pension scheme, and access to sports facilities. International staff may be eligible for tax exemptions. The initial contract is for 18 months, with extension upon satisfactory performance.

Application: The deadline for applications is January 15, 2026. Applicants should submit a CV and motivation letter (maximum 3 pages total) via the WUR vacancy website. Only applications submitted through the website will be considered. No additional documents are required at this stage.

For more information, contact Svenja Fischer (Assistant professor for Stochastic Hydrology) at [email protected]. For procedural questions, contact Jessa Rozema at [email protected].

Keywords: flood forecasting, hydrology, machine learning, rainfall-runoff models, hydrological modelling, climate change, statistics, extreme events, water management.