Junior Data Scientist / Scientific assistant - Tree species detection using deep-learning
Immigration Policy Lab
Junior Data Scientist / Scientific assistant – Tree species detection using deep-learning
60%-80%, Zurich, fixed-term
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
- 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 in working with multimodal data fusion and high-resolution remotely sensed data is required.
- Proficiency in programming, particularly in Python, is essential.
- Knowledge of deep learning approaches and experience in using semantic segmentation or instance segmentation is desired.
- Knowledge of or interest in forestry is desired.
- Fluency in English (written and spoken)
- Strong collaboration and communication skills
Workplace
Workplace
We offer
- 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.
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
Curious? So are we.
We look forward to receiving your online application, please include:
- Letter of motivation, no more than 1 page in length, describing your motivation to apply for this specific posting, your career goals, as well as a short description that separately and clearly addresses how you possess each of the qualifications listed above
- Detailed CV
- Copy of study diploma or equivalent
- Transcript of records
- 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.
All applications received by 10 January 2026 will receive full consideration. The position will remain open until it is filled.
ETH strives to be a workplace free from discrimination and with equal opportunities for all.
Further information about us can be found on our website. Questions regarding the position should be directed to Mirela Beloiu Schwenke via email at mirela.beloiu(at)usys.ethz.ch, or to Ariane Hangartner ariane.hangartner(at)usys.ethz.ch (for administrative questions).