Eawag, the Swiss Federal Institute of Aquatic Science and Technology, is an internationally networked aquatic research institute within the ETH Domain (Swiss Federal Institutes of Technology). Eawag conducts research, education and expert consulting to achieve the dual goals of meeting direct human needs for water and maintaining the function and integrity of aquatic ecosystems.
The Department of Systems Analysis, Integrated Assessment and Modelling (Siam) is announcing a
Postdoctoral researcher position in data driven and conceptual rainfall-runoff modelling
to conduct research on comparing data driven and conceptual models in hydrological applications.
The project focuses on rainfall-runoff modelling of Swiss catchments and pursues the following main objectives: 1) benchmarking the performance of data-driven models over conceptual models using a wide range of data (including high spatially and temporally resolved precipitation, and satellite observations). The comparison will use a wide range of conceptual models generated with the flexible modelling framework SUPERFLEX and a wide range of data-driven models, including multiple LSTM architectures; and 2) learning from the tradeoffs between data-driven and conceptual models and improving conceptual models by incorporating missing mechanisms. In order to gain knowledge from data-driven models, methods of “interpretable machine learning” will be used, an emerging field of research that has seen limited applications, particularly in environmental modelling.
The setup of data-driven models will be supervised by Dr Chaopeng Shen (Associate Professor at Pennsylvania State University), who will spend a 9 months sabbatical at Eawag starting in Summer 2020. The setup of the conceptual models will be supervised by Dr Fabrizio Fenicia (group leader at Eawag).
The successful candidate will contribute to perform the analysis, to the interpretation of results and to their publication as peer-reviewed journal articles and conference presentations.
- PhD (or equivalent international degree) in hydrology, environmental engineering, machine learning, or a similar field
- Demonstrated analytical, written and verbal communication skills and scientific excellence
- Excellent quantitative, computational, and programming skills (e.g., Fortran, MATLAB, Python)
- Ability to work independently and as part of a larger team
- Ability to think critically and innovatively
- Strong interest in interdisciplinary studies
- Domain knowledge in machine learning and hydrology
This position reports to Dr Fabrizio Fenicia. The appointment is 100% for two years. The anticipated (negotiable) start date is June 2020. Closing date is 31 March 2020
, but the screening of applicants will begin immediately.
Your application should include a CV, a motivation letter, copies of academic qualifications and the names and contact information for three references.
We look forward to receiving your application. Please send it through the Eawag webpage, any other way of applying will not be considered. A click on the button below will take you directly to the application form.