Offer DescriptionPostdoctoral position – Forest health assessment using data fusion approachesThe 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.Project backgroundis 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.Job descriptionThe 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:

• Design multimodal data fusion techniques to integrate multispectral, LiDAR, field data, and other data for enhanced forest monitoring.
• Develop deep learning (e.g., Transformers and CNNs) algorithms to integrate various datasets, such as tree species annotations, climate, forest structure, lidar and topography data, into deep learning (e.g., Transformers and CNNs) algorithms.
• Analyze the importance of environmental variables on the deep learning model accuracy.
• Establish species-specific links between early warning signals and tree decline.
• Generate forecasting maps indicating high-risk areas for forest decline.
• Collaborate with ecologists and remote sensing specialists to refine models for specific forest ecosystems.
• Publish research findings in high-impact scientific journals and present them at conferences.

Profile

• PhD in Remote sensing, Geoinformatics, Data Science, Forestry, Environmental Sciences, or a related field, from an internationally recognized university.
• Strong background in forestry, geoinformatics and remote sensing is required.
• Knowledge of deep learning approaches and experience in using semantic segmentation or instance segmentation is desired.
• Experience in working with multimodal data fusion and high-resolution remotely sensed data is required.
• Proficiency in programming, particularly in Python, is essential.
• Fluency in English (written and spoken)
• Strong collaboration and communication skills

We offer

• Opportunities to engage in cutting-edge research with the potential for high impact in the fields of forestry and computer vision.
• Opportunities for professional development.
• Opportunities to engage with different communities bridging data science, remote sensing and forest research, leading to high-impact publications.
• You will be part of a highly motivated, diverse, friendly, and collaborative team.
• You will be able to attend relevant (inter-) national conferences to increase your visibility and present the project outcomes.

Terms of employmentThe 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 value diversityIn line with , ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value diversity and nurture a working and learning environment in which the rights and dignity of all our staff and students are respected. Visit our to find out how we ensure a fair and open environment that allows everyone to grow and flourish.Curious? So are we.We look forward to receiving your online application including:

• Letter of motivation, no more than 1 page in length, describing your (1) career goals, as well as a (2) short description of your experience in using deep learning for image analysis
• Detailed CV (max. 3 pages)
• Publication list
• Copy of PhD diploma or equivalent
• Contact details of 2-3 references

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 .About ETH ZürichETH Zurich is one of the world’s leading universities specialising in
science and technology. We are renowned for our excellent education,
cutting-edge fundamental research and direct transfer of new knowledge
into society. Over 30,000 people from more than 120 countries find our
university to be a place that promotes independent thinking and an
environment that inspires excellence. Located in the heart of Europe,
yet forging connections all over the world, we work together to
develop solutions for the global challenges of today and tomorrow.Where to apply WebsiteRequirementsResearch Field Agricultural sciences Years of Research Experience 4 – 10Research Field Computer science Years of Research Experience 4 – 10Research Field Environmental science Years of Research Experience 4 – 10Research Field Environmental science Years of Research Experience 4 – 10Additional InformationWebsite for additional job detailsWork Location(s)Number of offers available 1 Company/Institute ETH Zürich Country Switzerland City Zurich Postal Code 8006 Street Rämistrasse 101 GeofieldContact CityZurich WebsitePostal Code8006STATUS: EXPIREDShare this page