The chair of Forest Resources Management (FORM), invites applications for a PhD position in forestry and remote sensing: Tree species monitoring (100%) funded by the Swiss National Science Foundation (SNSF) COST Action: CA20118 – 3DForEcoTech – Three-dimensional forest ecosystem monitoring and better understanding by terrestrial-based technologies.
Project background: Forests provide numerous ecological, economic, and climatic benefits, yet they are highly vulnerable to climatic extremes. Actively managing forest ecosystems for resilience and adaptation is crucial to ensure that forests continue providing these benefits. Forest monitoring – in particular tree species identification – is essential for assessing biodiversity, understanding forest resilience to climate change, and developing forest management strategies.
You will work in a multidisciplinary project combining aerial imagery, ground-truth data and terrestrial lidar data from Swiss-, European, and Global forests, using deep learning methods to identify the best ways forward for a first of its kind automatic tree species detection model.
This will include
- creation of a database for tree species labels that form the basis for the development of Convolutional Neural Network (CNN) based -object detection and -instance segmentation approaches to detect trees species from the overstory in remotely sensed imagery;
- assessing the transferability of these models to different forest types across Europe; and
- evaluating the capabilities of terrestrial lidar to detect saplings from the understory, and developing standard workflows for doing so.
In line with the milestones and deliverables of the project you will develop your doctoral thesis.
- You will process current forest inventory data and high-resolution imagery to create a database of tree species labels.
- You will collect data at our field sites using a drone and/or terrestrial lidar (no prior experience required).
- You will interact with forest practitioners and scientists working on similar topics.
- You will contribute to, and participate in the various activities of the group, including related research and teaching activities.
- A MSc (or equivalent) in Forestry, Forest Sciences, Environmental Sciences (Major Forest and Landscape Management), Geoinformatics, or a closely related field.
- Knowledge of GIS (QGIS or ArcGIS).
- Good programming skills, preferably in Python, are desired.
- Excellent written and oral communication skills in English.
- Ability to work independently and collaboratively in a team environment.
- Highly organized with excellent communication and interpersonal skills.
- Experience in processing lidar data is a plus.
- Opportunities to engage in cutting-edge research with the potential for high impact in the fields of forestry and deep learning.
- 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.
- You will be involved in the supervision of students theses projects and teaching activities of the lab.
We value diversity
In line with our values, 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.
Curious? So are we.
Your complete application consists of the following:
- Cover letter, no more than 2 pages in length, describing:
- your reasons for pursuing a doctorate
- your motivation to apply for this specific position
- a short description that separately and addresses how you possess each of the qualifications listed above
- Detailed CV, including a publication list (if applicable)
- Degree certificates and transcripts (BSc, MSc, and any other degrees you may hold)
- Names and contact details of 2-3 references
Please note that we exclusively accept applications submitted through our online application portal. All documents must be in PDF format.
The desired starting date is August 1st, 2023. A different starting date can be negotiated. Applications will be considered on a rolling basis and closed once a suitable candidate was found. However, applications received by July 9th will be given full consideration. The place of work will be ETH Zürich, Switzerland. For further information, please contact Ms. Ariane Hangartner; at email@example.com (no applications by e-mail)
About ETH Zürich: ETH 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.
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