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

Eidgenössische Forschungsanstalt für Wald, Schnee und Landschaft WSL; Birmensdorf (ZH); On-site; CHF 70,000 - 110,000; Full time

Job summary

The Swiss Federal Institute for Forest, Snow and Landscape Research WSL is seeking a PostDoc in Machine Learning and Urban Water Scarcity modelling. You will develop ML methods (LSTM, Graph Neural Networks) and work with weather/climate models to forecast water scarcity in Swiss cities, publishing results in scientific journals.

Applicants should hold a doctoral degree in environmental sciences or data sciences, have experience with hydrometeorological and urban modelling, and be fluent in

Qualifications

• You 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.
• You are fluent in English and have an above-average publication record for your career stage.

Responsibilities

• You will work at the interface of various models with end-users and stakeholders and publish outcomes in scientific journals.
• You will develop ML methods (LSTM, Graph Neural Networks) for water scarcity forecasting and climate applications.
• You will collaborate within an international research team and with ETH Domain partners.

Skills

LSTM

Graph Neural Networks

Python

R

Weather models

Probabilistic forecasts

Big Data

Web apps

English fluency

Education

Doctoral degree in environmental sciences or data sciences

Tools

Python

R

Job description

The Swiss Federal Institute for Forest, Snow and Landscape Research WSL is part of the ETH Domain. Approximately 600 people work on the sustainable use and protection of the environment and on the handling of natural hazards.

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

The WSL has initiated the Research Program “NOVA-City: Nature-based and Open Solutions for Viable, Adaptive Cities” (2027-2028) with the focus on three medium-sized cities in Switzerland and how to ensure long-term resilience, environmental sustainability, and a high quality of life under changing climatic, ecological, and societal conditions. The objective of the Nova-City project WaterScarCity (Water Scarcity Forecasting for Swiss Cities) is to help cities prepare against the challenges imposed by more frequent and intense heat and drought conditions using probabilistic subseasonal forecasts. It is located at the Research Unit Mountain Hydrology and Mass Movements, which develops novel operational hydrological forecast systems.

You 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.

You 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.

The 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.

Zürcherstrasse 111, CH-8903 Birmensdorf