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Research Associate for Remote Sensing and Machine Learning for Modeling Drought-Related Forest Damage

Full time Data Science Machine Learning Data Management Biostatistics DevOps

Job Description

Swiss Federal Institute for Forest, Snow and Landscape Research WSL
The Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), with around 600 employees, is part of the ETH Domain. It focuses on the sustainable use and protection of the environment and on the responsible management of natural hazards.
The Forest Resources and Forest Management research unit records and analyzes changes in the landscape and forest. In the Forest-LENS project, we are investigating drought symptoms in beech stands. This is based on an existing Sentinel-2 and machine learning-based processing pipeline for recording such symptoms, which is being expanded spatially and methodologically. A particular focus is on the influence of forest structure on the drought resilience of beech trees. To this end, we combine satellite-based drought indicators with forest structure information derived from LiDAR data, as well as other environmental and site data. The position is affiliated with the GIS research group, and we are seeking a candidate for a two-year contract, starting immediately or by agreement.

Research Associate for Remote Sensing and Machine Learning for Modeling Drought-Related Forest Damage

You hold a PhD in Environmental Sciences, Forestry, or Remote Sensing and have proven experience as a data scientist. We also expect excellent knowledge of spatial modeling with machine learning, sound experience in processing Sentinel-2, PlanetScope, and LiDAR data, and in-depth knowledge of modeling drought symptoms in forest ecosystems. Excellent programming skills in Python and experience handling large raster and vector datasets are required. Furthermore, you describe yourself as a dedicated, independent, and meticulous individual who implements complex spatial analyses in a goal-oriented manner and presents your results in a way that is understandable for both scientific and practical applications. You are fluent in both English and German (at least at level B1) and have experience in scientific publishing.

Please submit your complete application to Beatrice Lamprecht, Human Resources WSL, by uploading the required documents via our application portal. Applications submitted by email or post will not be considered. For any questions, please contact Andri Baltensweiler (andri.baltensweiler@wsl.ch). At WSL, diversity and inclusion are core values. We are committed to gender equality and fostering an open and inclusive work environment.

Zürcherstrasse 111, CH-8903 Birmensdorf
Eidgenössische Forschungsanstalt für Wald, Schnee und Landschaft WSL
About Eidgenössische Forschungsanstalt für Wald, Schnee und Landschaft WSL

The Swiss Federal Institute for Forest, Snow and Landscape Research WSL deals with the use, design and protection of natural and urban habitats. It develops contributions and solutions so that people can use landscapes and forests responsibly and deal carefully with natural hazards, which occur particularly in mountainous countries. The WSL occupies a leading international position in these research areas and provides the basis for sustainable environmental policy in Switzerland. The WSL is a federal research center, belongs to the ETH domain and employs around 600 people. In addition to the headquarters in Birmensdorf and the WSL Institute for Snow and Avalanche Research SLF in Davos, branch offices in Lausanne and Bellinzona have been promoting local synergies and dialogue with practitioners since 1991 and in Sion since 1996. Since its beginnings, the WSL has taken Switzerland's regional character into account. As early as 1888, test areas were set up in a wide variety of forests to learn more about their (wood) yields. Today, the WSL manages and researches over six thousand test and research areas, including large test facilities for avalanches, rockfalls or debris flows, experiments on the effects of climate change on forests, and areas affected by natural events such as storms or forest fires.

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