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PhD on Hydrological model-data interaction and machine learning for headwater catchment analyses
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ESP Global Services Limited —Basel, BS
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ETH Zürich —Basel, BS
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Capgemini ERD - Life Science Client Partner
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Own and grow key client relationships within the life sciences sector, acting as the primary point of contact for strategic accounts.

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

Universität Basel -
Basel, BS

Jetzt bewerben

Details zur Stelle

100%
vor 1 Tag

Qualifikationen

Management-Erfahrung
Doktorat
Schreibfähigkeiten
Datenanalyse
Englisch
Master
Python

Vollständige Stellenbeschreibung

The Hydrogeology research group at the Department of Environmental Sciences of the University of Basel is offering a fully funded PhD position within the international ANR–SNSF project FutureFlow.

Headwater catchments form the uppermost parts of river networks and represent a substantial fraction of the European hydrological system. Although relatively small, they play a critical role in controlling water resources and sustaining downstream ecosystems. Their functioning is largely governed by the aquifers they host, which regulate water storage and release to streams, sustaining flows during dry periods and buffering climatic variability.
Despite their importance, headwater systems remain poorly understood and difficult to predict, due to complex interactions between geology, topography, and climate, and limited observational data. In the context of increasing droughts and pressure on water resources, this lack of understanding limits our ability to anticipate future changes and design management strategies. Addressing these challenges requires new modelling approaches capable of representing groundwater processes across large and diverse catchments.
The FutureFlow project proposes to transfer concepts from software engineering-such as multi-fidelity modelling and adaptive model selection (model switching)-to hydrology. The objective is to develop flexible and scalable modelling frameworks capable of selecting, combining, and calibrating models of different complexity, to better represent groundwater dynamics and improve predictions at the European scale under climate change.

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 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.

You will specifically:

Contribute to the development of the multi-fidelity modelling platform HydroModPy,
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.

Your profile

Required Qualifications:

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.

We offer you

The project brings together a consortium of leading European institutions, including the University of Neuchâtel, University of Rennes 1, CNRS, BRGM, ENS Paris, Eawag and the University of Basel. The successful candidate will be part of a cohort of 4 PhD students, 2 research engineers, and 7 principal investigators, working collaboratively across partner institutions. The project will be supervised by Prof. Oliver S. Schilling, Head of the Hydrogeology Research group at the University of Basel, and co-supervised by Prof. Clément Roques, Université de Neuchâtel. The position is based at the Department of Environmental Sciences of the University of Basel in Basel in Switzerland, fully funded for 4 years, and includes a 3-month stay at the University of Utrecht and a 3-month stay at BRGM.

At the Department of Environmental Sciences, you will have access to a comprehensive pool of required informatic and field infrastructure, as well as technical support staff. You will be integrated in the Graduate School of the Department of Environmental Sciences as well as the Swiss Water-Earth Systems PhD School.

Information on past and ongoing research projects can be found at the website of the Hydrogeology Research Group.

Application / Contact

We only accept online applications submitted through the online application platform; use the link below to access the online application form. Your application should include a motivation letter (max. 1 page), a CV, a copy of your MSc diploma, contact details of at least two references, and, if applicable, a description of a research project that you conducted or contributed to, stating the projects rationale, results, and your contribution (max. half page).

For further information, please contact Prof. Dr. Oliver S. Schilling, [email protected]. The position is set to start in September 2026, or on agreement but no later than January, 2027. Applications will be reviewed on a rolling basis, but the position will remain open until filled.

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learning development Jobs in basel bs

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

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