Postdoc: ML for Urban Water Scarcity Forecasts

Eidgen; 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 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