PostDoc in Machine Learning and Urban Water Scarcity modelling 80 - 100% (f/m/d)

pYou are an early career scientist with an interest in multidisciplinary research and excellent knowledge in Machine Learning methods like LSTM and Graph Neural Networks. You are expected to have a background in the use and application of weather and climate model output, with strong expertise and experience in programming (R, Python) and in the application of statistical and probabilistic forecasts. You will work on the interface of the various models developed at WSL, ETH, EMPA and EAWAG with sub-seasonal forecasts. Experience with the development of user-friendly Web-based applications in consultation with end-users and stakeholders is a plus. You publish your outcomes in scientific journals. /ppYou have a doctoral degree in environmental sciences or related discipline including data sciences. You have experience in hydrometeorological and spatial temporal (urban) modelling and in-depth knowledge of applied data sciences and processing of Big Data. The ability to work independently, conceptually and precisely as well as flexibility and willingness to perform are among your strengths, as well as good communication skills and cooperation in an international and dynamic research team. You are fluent in English and have according to your career stage an above average publication record. /ppDr. K. Bogner ( ) will be happy to answer any questions or offer further information. /ppThe WSL strives to increase the proportion of women in its employment, which is why qualified women are particularly called upon to apply for this position. /p #J-18808-Ljbffr