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PhD in Stochastic Modelling and Data Analysis in Hydrology, Netherlands


The goal of this project is to achieve more reliable predictions of hydrological signatures (peaks, flow duration curves etc.) in natural catchments using improved rainfall-runoff models and inference algorithms. 

It is well known that rainfall- and runoff-time series obey remarkably universal scaling laws. This means that the long-range features of these time series might not depend on many details of the underlying small scale processes and thus be amenable to relatively simple stochastic modelling, which we want to exploit to arrive at more reliable runoff forecasts, in particular of extreme events. 

Calibrating stochastic models to measured data and quantifying the ensuing uncertainty is a very computation-intense task, which we’ll tackle with our newly developed inference algorithms. They will yield more reliable estimates for parameter uncertainty and thus increase the reliability of our predictions. 

A simple way of calibrating stochastic models is to compare only certain signatures of measured and simulated hydrographs. This is also interesting for un-gauged catchments, where we can infer certain signatures from the climate and geology. In hydrology, scaling laws have hardly been considered in the context of signatures so far. We expect the exponents of these laws to contain important information about the underlying catchment. 

For further information on the project, please contact Dr Carlo Albert ( 

We are looking for a qualified candidate with a strong mathematical background and an interest in data analysis and programming. Knowledge in statistical physics is advantageous. Excellent spoken/written English is a requirement. 

The position is funded for three years, starting in March 2017 or as agreed upon. The PhD enrolment will be at the ETH Zurich. 

Eawag offers a unique research and working environment and is committed to promoting equal opportunities for women and men and to support the compatibility of family and work. Applications from women are especially welcome. For more information about Eawag and our work conditions please consult and 

Further Information

Application Deadline : 13 February 2017

PhD in Stochastic Modelling and Data Analysis in Hydrology : PDF

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Posted on 2017-01-09 09:31:16

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