Our team and mission

Reporting to the Head of the Explore Office in the ESA Φ-lab, you will work in close cooperation with other staff in the Directorate of Earth Observation Programmes.

You will be part of the ESA Φ-lab. Our mission is to accelerate the future of Earth Observation (EO) by embracing disruptive innovation and serving as the catalyst for disruptive and transformative innovation in the sector.

Our vision is to become an EO innovation hub connecting EO with a growing ecosystem of disruptive and transformative innovation like AI, ML, quantum computing, edge computing, metamaterials and photonics. Many of the challenges posed by new technologies remain to be tackled at scientific, application and capability levels to deliver maximum value from satellite-derived EO assets for our climate, society and economy. The Φ-lab will bring together early-career and senior researchers from a variety of disciplines across EO and disruptive/transformative innovation to contribute to the development of innovative EO solutions.

We offer:

• a stimulating multinational, interdisciplinary and open work environment;
• access to high-performance computing infrastructure and unparalleled EO and technology expertise;
• a unique opportunity to work on innovative solutions to address global challenges;
• freedom and focus to conduct creative research while making an impact in relation to ESA’s strategy;
• a wide network of relationships and collaboration with top academia, industry and research centres;
• the opportunity to contribute to the Φ-lab strategy and activities.

As an internal research fellow within the Φ-lab, you will invest your time mainly in the agreed research topics, but will also provide support to the Φ-lab’s industrial and internal activities. You will also be involved in mentoring of members of our research network, as well as outreach activities, all generally related to your research topic though not exclusively.

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

Field(s) of activity/research for the traineeship

This fellowship aims to advance the development and application of geospatial foundation models tailored to multimodal and multiscale EO datasets. The goal is to unlock new capabilities in understanding complex environmental systems and support transformative solutions for climate resilience, sustainability and planetary health. For example, you will leverage large-scale AI models to:

• integrate heterogeneous EO data sources, such as satellite, aerial and in-situ data, across spatial and temporal scales;
• enable zero-shot or few-shot learning for rapid adaptation to new EO tasks such as disaster response, biodiversity monitoring or land-use change detection;
• support generative EO applications such as synthetic data creation, gap-filling, and simulation of future Earth scenarios, for example “what if” scenarios;
• develop advanced embedding strategies and neural compression techniques to efficiently represent high-dimensional EO data for scalable learning and inference.

As part of your application, please provide a research proposal of no more than five pages, answering the following questions:

• What EO challenge would you like to address using foundation models?
• Which disruptive technologies or paradigms (specific architectures, lightweight adaptation methods, xAI, interpretable-physic awareness, uncertainty quantification, etc.) will you explore?
• Why is this research proposal important to society?

In particular, you will:

• undertake advanced research activities exploring and expanding the use of disruptive and transformative innovation to develop new frameworks and solutions around geospatial AI foundation models for EO. Research may cover a wide range of innovative topics and will focus on:
  • exploring innovative methodologies and technologies;
  • developing novel methods of new technology implementation, for example ML models, for high-level products;
  • contribute to the development and curation of open datasets and tools enabling the community to develop its own AI for applications and research;
• support the definition and implementation of rapid prototyping activities, research sprints and open challenges of innovative EO solutions addressing upcoming lab activities and wider strategy;
• participate in internal research sprints to explore, test and validate novel EO concepts, algorithms and workflows in a fast-paced, collaborative environment;
• engage with the innovation ecosystem to promote the uptake of new techniques and capture the latest developments in EO and disruptive/transformative innovation;
• publish the research project outcomes in high-impact journals;
• drive collaboration within the Φ-lab community and ESA internal teams to promote the uptake of the new techniques and solutions;
• contribute to the Φ-lab strategy, activities and outreach on disruptive technologies for EO;
• maintain a continuous dialogue with the scientific community, including major international programmes and initiatives in the field;
• support the Φ-lab’s daily activities and research network of young graduate trainees, national trainees, interns and visiting researchers, as applicable.

Technical competencies

Behavioural competencies

Result Orientation
Operational Efficiency
Fostering Cooperation
Relationship Management
Continuous Improvement
Forward Thinking 

For more information, please refer to ESA Core Behavioural Competencies guidebook

Education

You should have completed within the past five years or be close to completing a PhD in a relevant field such as data science, AI, computer science, machine learning, Earth system science or climate, with a thesis subject relevant to the description of the tasks outlined above.

Additional requirements

In addition to your CV and your motivation letter, please prepare a research proposal of no more than 5 pages. This proposal should be uploaded to the "additional documents" field of the "application information" section.

You should have:

• Sound knowledge of themes of interest, such as EO, AI, ML and ICT revolution technologies;
• Proven experience in the field of geospatial foundation models, including contributions to top-tier journals or conferences;
• The ability to think outside the box and explore new avenues, with natural curiosity and a passion for new subjects and research areas;
• Experience with one or more general-purpose programming languages, for example Python, and general-purpose deep learning frameworks, such as PyTorch;
• An interest in and ability to share knowledge with other ESA organisational units.

You should also have good interpersonal and communication skills and should be able to work in a multi-cultural environment, both independently and as part of a team. 

Your motivation, overall professional perspective and career goals will also be explored during the later stages of the selection process.