University of Basel is Offering a Fully Funded PhD in Hydrological Model-Data Integration and Machine Learning for Headwater Catchments @Data Integration and Machine Learning for Headwater Catchments

The University of Basel has announced a fully funded PhD position in Hydrology focusing on hydrological model–data interaction and machine learning applications for headwater catchment analysis. The position is offered within the Hydrogeology Research Group at the Department of Environmental Sciences and forms part of the international ANR–SNSF research initiative FutureFlow.

The PhD is set to begin in September 2026, or by agreement, and is fully funded for four years.

Research Context: Understanding Headwater Catchments in a Changing Climate

Headwater catchments represent the uppermost segments of river systems and account for a significant portion of Europe’s hydrological landscape. Despite their relatively small spatial footprint, they play a critical role in:

• Regulating regional water resources
• Sustaining downstream river ecosystems
• Controlling seasonal and drought-period water availability
• Buffering climate variability through groundwater storage systems

These systems are primarily governed by aquifers that regulate both water storage and release to streams.

However, headwater catchments remain poorly understood due to:

• Limited observational and monitoring data
• Complex interactions between geology, climate, and topography
• Difficulty in predicting hydrological behaviour under changing climatic conditions

The lack of reliable models constrains long-term water resource planning, particularly in the context of increasing drought frequency and climate stress.

FutureFlow Project: Advancing Multi-Fidelity Hydrological Modelling

The PhD position is embedded in the FutureFlow project, an international initiative that introduces concepts from software engineering into hydrological modelling. The project focuses on:

• Multi-fidelity modelling approaches
• Adaptive model selection and model switching
• Scalable frameworks for groundwater system simulation
• Improved prediction of hydrological responses under climate change

The overarching objective is to develop flexible modelling systems capable of combining and calibrating models of varying complexity to better represent groundwater–surface water interactions across European catchments.

PhD Research Objectives and Technical Contributions

The successful candidate will contribute to advancing data-driven hydrological modelling, with a strong emphasis on machine learning and hybrid simulation frameworks.

Key research tasks include:

• Development of multi-fidelity modelling approaches for headwater systems
• Contribution to the HydroModPy modelling platform
• Application of machine learning techniques to estimate hydraulic properties across European catchments
• Evaluation of hydrological validation methods for catchment property identification
• Development of hybrid models for hindcasting and forecasting hydrological behaviour in ungauged catchments
• Use of climate storyline approaches to assess vulnerability to extreme hydrometeorological events

The project is expected to improve both predictive capability and conceptual understanding of groundwater and surface water interactions.

Scientific and Practical Impact

The outcomes of the research are designed to serve both academic and applied communities by:

• Improving hydrological predictions under climate change scenarios
• Enhancing understanding of groundwater–surface water coupling
• Supporting water resource management in drought-prone regions
• Advancing computational hydrology through machine learning integration

The project contributes directly to European-scale efforts in climate resilience and water security planning.

Candidate Profile and Required Skills

Applicants are expected to hold a strong academic background in environmental and computational disciplines.

Required Qualifications

• MSc degree in Hydrology, Hydrogeology, Data Science, Computer Science, or a related field
• Strong interest in environmental data analysis and numerical modelling
• Proficiency in Python programming
• Excellent written and spoken English skills

Desirable Attributes

• Interest in scientific communication and interdisciplinary research
• Experience with hydrological or environmental modelling systems
• Familiarity with machine learning or data-driven modelling approaches

The role requires a strong capacity for independent research as well as collaborative work within international teams.

Research Environment and Institutional Collaboration

The PhD candidate will join a large European research consortium including:

• University of Basel
• University of Neuchâtel
• University of Rennes 1
• CNRS
• BRGM
• ENS Paris
• Eawag
• University of Utrecht (research stay)

The selected candidate will be part of a cohort comprising:

• 4 PhD researchers
• 2 research engineers
• 7 principal investigators

Supervision will be provided by:

• Prof. Oliver S. Schilling (University of Basel)
• Prof. Clément Roques (University of Neuchâtel, co-supervisor)

Training, Mobility, and Research Opportunities

The position is based in Basel, Switzerland, within the Department of Environmental Sciences. It includes access to advanced research infrastructure, technical support, and integration into:

• The Graduate School of Environmental Sciences
• The Swiss Water-Earth Systems PhD School

Additional research mobility opportunities include:

• A 3-month research stay at the University of Utrecht
• A 3-month research stay at BRGM (French Geological Survey)

These placements are designed to strengthen interdisciplinary collaboration and applied research experience.

Application Process and Required Documents

Applications must be submitted online through the University of Basel application platform. Required documents include:

• A one-page motivation letter
• Curriculum vitae (CV)
• MSc diploma copy
• Contact details for at least two referees
• Optional: description of a previous research project (maximum half page), including objectives, results, and personal contribution

Applications are reviewed on a rolling basis until the position is filled.

Contact and Deadline Information

For further inquiries, applicants may contact:

Prof. Dr. Oliver S. Schilling
Email: oliver.schilling@unibas.ch

The position will begin in September 2026 or by agreement, but no later than January 2027.

Conclusion

The University of Basel PhD opportunity represents a highly interdisciplinary research position at the intersection of hydrology, machine learning, and computational modelling. By integrating advanced data-driven approaches with hydrological science, the project aims to improve understanding and prediction of headwater catchment systems under increasing climate pressure.

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Disclaimer: Global South Opportunities (GSO) is not the organization offering this opportunity. For any inquiries, please contact the official organization directly. Please do not send your applications & CVs to GSO, as we are unable to process them. Due to the high volume of emails, we receive daily, we may not be able to respond to all inquiries. Thank you for your understanding.