Postdoc on hydrological real-time modelling and forecasting

The Postdoc will be conducting cutting-edge research within operational hydrological modelling and its intersection with machine learning. The objectives include (1) the propagation of an ensemble of weather forecasts in an operational hydrological model, (2) the development of novel data assimilation frameworks, (3) the generation of flood hazard parameters based on the hydrological model and (4) the handling of the dataflow of weather data and modelling results with the collaborating institutions. As a Post Doc at GEUS, you will contribute to the further development of the national coupled groundwater surface water model (DK-Model).   

The core of the modelling system to be developed is the DK-Model. Building on top of the physically-based model, the Postdoc will exhaust all available observations, i.e. in situ data on groundwater levels, streamflow and water levels in streams as well as satellite derived inundation maps, using machine learning. The purpose of employing machine learning models is to achieve the highest possible accuracy of the hydrological forecast via novel data assimilation frameworks, but also to translate the forecast variables, i.e. groundwater head and streamflow, into flood hazard parameters. The candidate should expect to broaden her/his existing field by covering elements of hydrological modelling, machine learning, data analysis and data management. Research findings should be published in leading journals within the field.