Estonian National Graduate Trainee in AI/Machine Learning Mapping of Forestry Biodiversity - European Space Agency (Frascati)

About this position

The summary below is published by European Space Agency on the official vacancy notice. Our own analysis — salary realism, comparable openings, career trajectory, language profile — follows further down the page.

Key responsibilities

According to the vacancy notice, the role centres on the following duties:

• Develop forest carbon and structural/functional biodiversity indicators for protected areas
• Map land cover and land cover change within protected boundaries using long-term EO time series
• Compare trends inside protected areas with surrounding unprotected lands
• Evaluate ecosystem condition by tracking vegetation health, fragmentation, or recovery after disturbance
• Summarise key findings to inform international/national policies aimed at forest conservation
• Utilise ESA-supported cloud computing platforms for research

Are you eligible?

Check these requirements from the notice before investing time in an application:

• Education: Master's degree in Forestry, Remote Sensing, Data Science, Environmental Science, or a related engineering discipline (just completed or in final year)
• Key skills: Knowledge of Earth observation for land cover mapping and multi-temporal satellite data processing, Strong programming skills (e.g. R, Python), Geospatial data processing, Machine learning and deep learning for image and time series analysis, Experience with cloud or high performance computing environments, Ability to communicate results clearly in writing and presentations, Ability to collaborate effectively with international partners, Good interpersonal and communication skills, Ability to work in multicultural environment
• Eligibility: Citizen of an ESA Member State or Associate Member (NGT Programme is funded by an individual ESA Member State or Associate Member)

Selection process

The notice describes the following selection steps:

• Assessment against position requirements
• Evaluation of background and knowledge of space policies and related activities of sponsoring State
• Later stages exploring motivation, overall professional perspective and career goals

Contract and working arrangements

Contract duration: Graduate trainee programme (duration not specified in text).

Working arrangements: On-site at ESRIN, Frascati, Italy.

Hiring unit: Earth Observation Programmes Directorate, Applications Section, Green Solutions Division.

Position details

Reference

1437025433

Last Verified

16 September 2026

Position overview

This is the official EU Careers listing for Estonian National Graduate Trainee in AI/Machine Learning Mapping of Forestry Biodiversity at ESA based in Frascati (Other). The vacancy reference is 1437025433.

Job Requisition ID:20892

Date Posted:15 September 2026

Closing Date:13 October 2026 23:59 CET/CEST

Publication:External Only

Directorate:Earth Observation Programmes

The ESA National Graduate Trainee (NGT) Programmes offer recent Master’s graduates the opportunity to gain practical, hands-on experience at ESA across a range of space-related disciplines. The programmes are essentially similar to ESA’s own Graduate Trainee Programme, notably in offering the same employment conditions. They however differ in one important respect: each NGT Programme is funded by an individual ESA Member State or Associate Member under a bilateral partnership agreement concluded with ESA. As a result, a representative of the sponsoring State participates in the selection process. In addition to assessing candidates against the requirements of the position, the selection may also include an evaluation of their background and knowledge of the space policies and related activities of the sponsoring State.

Location

ESRIN, Frascati, Italy

Our team and mission

The Applications Section primarily engages in research and development (R&D) focused on Earth observation-based solutions that contribute to addressing the European and global international environment and development agenda. Our projects typically involve active collaboration with Early Adopters at every stage, including design, development, and validation. This ensures the seamless integration of innovative EO-based solutions into end-user operational systems and practices. The team is based at the ESRIN site of ESA and comprises nine Earth observation (EO) experts. Each expert specialises in overseeing specific domains where EO data are applied. The Applications team is part of the Green Solutions Division (eo4society - eo science for society)).

You are encouraged to visit the ESA website: http://www.esa.int

Field(s) of activity/research for the traineeship

About 21% of the world’s forest area is currently designated under legal protection. These forests are highly valuable for carbon storage and sustaining biodiversity. However, high annual rates of tree cover loss continue to this day, with impacts both on forest carbon stocks and their biodiversity. It therefore remains difficult to assess how effective protected area designation is as a forest conservation tool. The legal status of a protected area does not automatically mean that active protection is being implemented, particularly in regions with limited financial resources for conservation.

ESA’s Sentinel missions are widely used to monitor forests by providing frequent, consistent, and freely available satellite data on forests. In April 2025, ESA also launched the BIOMASS mission with a novel P-band synthetic aperture radar designed to deliver crucial information about forest conditions, changes, and their role in the carbon cycle. As an NGT, you will utilise BIOMASS mission data in synergy with several other EO missions (particularly the Sentinels) to achieve the following:

1. Develop forest (a) carbon and (b) structural/functional biodiversity indicators for protected areas. 2. Map land cover and land cover change within protected boundaries using long-term EO time series, comparing trends inside protected areas with surrounding unprotected lands. 3. Evaluate ecosystem condition, for example by tracking vegetation health, fragmentation, or recovery after disturbance. 4. Summarise key findings that will concisely and accurately inform international/national policies aimed at forest conservation.

You will utilise ESA-supported cloud computing platforms for conducting your research. By the end of the traineeship, you will have gained practical experience in scalable Earth observation processing, applied analytical methods for land monitoring, product validation and uncertainty assessment, and stakeholder-driven development in an international setting. You will also develop a broader understanding of how EO-derived forest information supports international policy and reporting frameworks.

Technical competencies

Knowledge of relevant technical/functional domains

Relevant experience gained during internships, project work and/or extracurricular or other activities

General knowledge of the space sector and relevant activities

Knowledge of ESA and its programmes/projects

Behavioural competencies

Education

You should have just completed, or be in the final year of your master’ s degree in Forestry, Remote Sensing, Data Science, Environmental Science, or a related engineering discipline.

Additional requirements

You should have good interpersonal and communication skills and should be able to work in a multicultural environment, both independently and as part of a team. Previous experience of working in international teams can be considered an asset. Your motivation, overall professional perspective and career goals will also be explored during the later stages of the selection process.

In addition to the standard requirements, the following would be considered an asset:

- Experience with Earth observation for land cover mapping and multi temporal satellite data processing; familiarity with forest ecosystems is an asset. - Strong programming skills (e.g. R, Python), including geospatial data processing. - Knowledge of machine learning and deep learning for image and time series analysis; experience with cloud or high performance computing environments is an asset. - Ability to communicate results clearly in writing and presentations and to collaborate effectively with international partners.

Application timeline

This vacancy was first listed on 15 September 2026, 1 day ago.

The application deadline is 13 October 2026, 25 days from today. Late applications are not accepted, so submit through the official EU Careers portal well in advance.

Last verified against the EU Careers feed on 16 September 2026.

Where to learn more

For headcount, mission, and other open vacancies at ESA see the ESA institution page; for the cost of living, correction coefficient, and other postings in Frascati see our Frascati location page.

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