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The Forest Resources Management group is seeking a postdoctoral researcher to join the UPSCALE project to develop data fusion algorithms able to quantify forest composition and forest health across large scales.
UPSCALE (2024-2028) is a 4-year multidisciplinary project led by ETH and WSL. It is funded by the Swiss National Science Foundation. UPSCALE will advance forest health and mortality monitoring by bridging methods and disciplines. We will integrate early warning signals for the detection of tree health decline, and mortality in trees and forests across spatial scales - from the tree to the landscape level. We will link high spatial and temporal resolution remote sensing data with traditional ground-based forest monitoring data, novel close-to-real-time tree and ecosystem level assessments, and belowground information to identify key proxies of forest drought stress, tree health, and mortality risk. Our work will provide a solid backdrop aiding decision-makers in prioritizing their silvicultural, and forest management planning activities, ensuring the sustainable provision of forest goods and ecosystem services under future climate conditions. In addition, there is a close collaboration with the EcoVision Lab of the Department of Mathematical Modeling and Machine Learning at the University of Zurich, which will facilitate the transfer of knowledge between the fields of computer vision and forestry.
The successful candidate will develop a comprehensive modeling approach to quantify tree species health and forecast tree species decline at a large scale, linking these metrics with forest composition. This role involves employing data fusion methods, integrating forest structural characteristics, climatic, and topographic variables with machine learning algorithms to predict areas at risk for reduced forest vitality and tree species decline.
Key Responsibilities:
Terms of employment
The position is renewable annually, up to a total of 3 years. The desired starting date is March 15, 2025.
All applications received by January 20, 2025 will receive full consideration. The position will remain open until it is filled.
We look forward to receiving your online application including:
All documents must be in PDF format and must not be compressed. Please note that we exclusively accept applications submitted through our online application portal.
For further information please contact Ariane Hangartner ariane.hangartner(at)usys.ethz.ch (no applications) or visit our website.
ETH Zürich is well known for its excellent education, ground-breaking fundamental research and for implementing its results directly into practice.
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