Junior Data Scientist / Junior Data Scientistess

Join Our Team as a Data Scientist

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

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

Our researchers are dedicated to developing cutting-edge algorithms and AI-based solutions for data processing and validation, providing scientific expertise for future remote sensing missions. The team's work bridges earth observation with applied forest monitoring, including:

• Tree species identification
• Forest structural changes
• Forest resilience assessments

We are increasingly focused on spectral and functional trait analysis to support biodiversity and genetic monitoring in forestry.

The TreeAI Global Initiative aims to extend the current database and map individual trees, which are essential for effective forest management. By utilizing aerial RGB imagery, we strive to create a cost-effective, automated system for detecting and identifying tree species, applicable across various forest monitoring scenarios.

In this role, you will support the TreeAI Global Initiative by developing data-driven methods to enhance large-scale tree monitoring. Your key responsibilities will include managing and expanding the TreeAI database and advancing deep learning workflows for tree species mapping, contributing to a scalable system for forest monitoring through model performance refinement and 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 datasets such as tree species annotations, climate, and topography into deep learning algorithms.
• Test deep learning models (Transformers and CNNs) for optimal accuracy using extensive datasets of over 110,000 tree species annotations, along with climatic, topographic, and lidar data.
• Evaluate the best algorithms developed for identifying tree species across large areas.

Qualifications

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

What We Offer

• Engagement in cutting-edge research with the potential for significant impact in the fields of forestry and deep learning.
• Opportunities for professional development.
• Collaboration with diverse communities bridging data science, remote sensing, and forest research, leading to high-impact publications.
• A supportive, motivated, and collaborative team environment.

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

Apply online using the form below. Only applications matching the job profile will be considered.

ETH strives to be a workplace free from discrimination, ensuring equal opportunities for all.

For further information about us, please visit our website. If you have any questions regarding the position, please contact Mirela Beloiu Schwenke via email at mirela.beloiu(at)usys.ethz.ch or Ariane Hangartner at ariane.hangartner(at)usys.ethz.ch for administrative inquiries.