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.
It is a full-time 3-year position focused on "hybrid models" that combine process-based (PB) and machine-learning (ML) models in hydrology.
This project aims to combine the recent advances of conceptual models, particularly the evolution from fixed model structures into flexible frameworks, with the elements that have contributed to the success of ML models in streamflow prediction.
The specific objectives of this research are to: (i) develop a software infrastructure that optimally combines mass-conserving conceptual model elements with network architectures typical of machine learning models, (ii) advance model selection and identification, and explore the trade-off between performance and interpretability with various kinds of architectures, (iii) advance process knowledge through applications to large hydrological datasets. The project relies on available software infrastructure in Python. The research requires a solid mathematical background, programming ability, and conceptualization skills.
The project is based at Eawag (Dübendorf). The supervising team includes Fabrizio Fenicia (Eawag), Dmitri Kavetski (University of Adelaide), and Grey Nearing (Google Research).
The deadline for applications is 10 October 2023 or until the position is filled. The start is planned as soon as possible.
a modern employer and offers an excellent working environment where staff can contribute their strengths, experience and ways of thinking. We promote gender equality and are committed to staff diversity and inclusion. The compatibility of career and family is of central importance to us. For more information about Eawag and our work conditions please consult www.eawag.ch
. Siam provides a friendly and stimulating research environment with the potential for interaction with researchers in statistical inference, computation and mathematical modelling.
We look forward to receiving your application. Please send it through this webpage, any other way of applying will not be considered. A click on the button below will take you directly to the application form.