Junior Data Scientist / Scientific assistant – Tree species detection using deep-learning

Junior Data Scientist / Scientific assistant – Tree species detection using deep-learning

Are you an ambitious data scientist with strong analytical and numerical skills, and expertise in geomatics, remote sensing, and data processing? We invite you to join our team and help shape the future of Earth observation in forest management.

Project background

You will join FORM (the Professorship of Forest Resources Management) and the team that focuses (1) on the acquisition, processing, and interpretation of satellite and drone data, as well as (2) on the development of operational applications for emerging intelligent earth observation technologies.

Our researchers develop cutting-edge algorithms and AI-based solutions for data processing and validation and provide scientific expertise for the implementation of future remote sensing missions. The team’s work bridges earth observation with applied forest monitoring, including tree species identification, forest structural changes, and forest resilience assessments, with a growing focus on spectral and functional trait analysis to support biodiversity and genetic monitoring in forestry.

The TreeAI Global Initiative focuses on extending the current database and mapping individual trees, which are essential tasks that support forest management. Using aerial RGB imagery, we aim to create a cost-effective, automated system for detecting and identifying tree species, with broad applications in forest monitoring.

Job description

You will support the TreeAI global initiative by developing data driven methods to enhance large scale tree monitoring. The role focuses on managing and expanding the TreeAI database and advancing deep learning workflows for tree species mapping. The position contributes to building a scalable system for forest monitoring by refining model performance and ensuring high quality geospatial data integration.

Key Responsibilities :

• Acquire and harmonize new spatial and aerial datasets for the TreeAI database.
• Support the development of the TreeAI database by integrating various datasets, such as tree species annotations, climate, and topography, into deep learning algorithms.
• Test deep learning models (Transformers and CNNs) for optimal accuracy using large datasets that include over 110,000 tree species annotations, along with climatic, topographic, and lidar data.
• Test the best algorithms developed for the identification of tree species over large areas.

Profile

We offer

The position is initially for one year, renewable for up to six years. The desired starting date is 15 February, or 1 March 2026 at the latest.

We value diversity and sustainability

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. Visit our Equal Opportunities and Diversity website to find out how we ensure a fair and open environment that allows everyone to grow and flourish. Sustainability is a core value for us – we are consistently working towards a climate-neutral future.

Curious? So are we.

We look forward to receiving your online application, please include :