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 and colleagues from the Directorate of Technology, Engineering and Quality. There may also be collaboration with scientists and engineers in other divisions at ESA. You will be part of the ESA Φ-lab. Our mission is to accelerate the future of Earth observation via transformative innovation* and commercialisation actions strengthening ESA Member States’ world-leading competitiveness. .Our vision is to be the “EO innovation hub”, connecting EO with a growing ecosystem of disruptive and transformative innovation such as AI, ML, quantum computing, edge computing, metamaterials, photonics, etc. Many of the challenges posed by new technologies still need to be tackled at the scientific, application and capability level 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 help develop 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.

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

Field(s) of activity/research for the traineeship

Main objectives of the Research Fellowship

Cognitive cloud computing in space (3CS) is emerging as a key paradigm for future Earth observation systems, enabling coordinated, event-driven operations across heterogeneous space assets through onboard intelligence, distributed computing and inter-satellite communications. By supporting capabilities such as autonomous tip-and-cue, event-based mission replanning, adaptive multi-sensor tasking and distributed information fusion, 3CS aims to significantly improve the responsiveness and autonomy of future EO missions. In this context, synthetic aperture radar (SAR) represents a strategic sensing modality thanks to its all-weather, day-and-night imaging capabilities, providing reliable information to trigger and support coordinated observations in rapidly evolving scenarios such as disasters, maritime surveillance and security monitoring. Recent advances in cognitive SAR have demonstrated the potential of AI methods operating directly on raw or partially processed data for tasks such as learned focusing, adaptive compression and information extraction. However, significant challenges remain, including the computational complexity of SAR processing chains, the limited robustness of current AI approaches, and the difficulty of deploying intelligent processing under stringent onboard resource constraints. In addition, significant exploration is still required to explore novel cognitive SAR and novel mission concepts enabled by onboard SAR processing. This fellowship will address these challenges by developing AI-based methods for onboard SAR data processing operating close to the sensing level, enabling real-time information extraction, event detection, autonomous re-tasking, and cooperative multi-sensor operations for next-generation missions, including Copernicus Sentinel-1 Next Generation, thereby supporting the transition towards autonomous and coordinated EO systems.

Research topic of interests include:

• advanced onboard SAR data processing architectures: target applications include but are not limited to onboard SAR data focusing, and extraction of insight from SAR unfocused data;
• cognitive SAR: novel mission concepts, applications and in-orbit data processing for cognitive SAR systems;
• onboard foundation models for SAR data processing: including multimodal foundation models;
• onboard SAR for 3CS systems: onboard SAR for tip-and-cue systems, in-orbit data fusion, etc.

Within your application, please provide a research proposal (no more than five pages) related to onboard AI for SAR missions:

• Describe the main research questions that you would like to address.
• Propose a research plan with a clear description of the methodology that you would like to adopt to answer those questions with specific reference to your previous experience and your publications on the topic, the novel concept that you plan to research, and a possible schedule.

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In particular, you will:

• undertake advanced research activities, exploring and expanding the use of disruptive/transformative innovation (AI, ML, disruptive computing, etc.) to develop new prototypes for onboard SAR data processing, cognitive SAR, and onboard SAR for 3CS systems. The research may cover a wide range of innovative topics:
  • exploration of innovative methodologies and technologies;
  • the development of novel methods, of new technology implementations, (e.g. ML models), of high-level products;
  • contribution to the development and curation of open data sets 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;
• 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 these 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 ESA Graduate Trainees, National Trainees, interns, visiting professors, experts and researchers, as applicable;
• collaborate closely with ESA’s Φ-lab and avionics and EEE Division for the identification, testing and benchmarking of hardware components for edge computing and learning;
• conduct research and implement prototypes of onboard AI for SAR;
• support ESA’s Φ-lab staff with external R&D activities related to onboard AI for SAR.

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 recently completed (within the past five years), or be close to completion of a PhD in a relevant field such as data science, AI, computer science, remote sensing, machine learning, electronics, embedded systems, aerospace engineering, Earth system science, climate etc., with the subject of your thesis being relevant to the description of the tasks outlined above.

Additional requirements

In addition to your CV and motivation letter, please prepare a research proposal of no more than five pages. When submitting your application, please upload the proposal under the “Research Outline” field in “My Documents.

You should have:

• sound knowledge of themes of interest, such as EO, AI, ML, and technologies in general;
• knowledge relevant to your research field in SAR signal processing and algorithms;
• 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 Tensorflow or PyTorch;
• the interest and ability to share knowledge with other ESA organisational units.

The following elements are considered an asset:

• Knowledge of onboard AI, edge computing for SAR systems
• Experience with hardware for edge computing (e.g., FPGAs, TPUs, VPUs and GPUs)

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.

The working languages of the Agency are English and French. A good knowledge of one of these is required. Knowledge of another Member State language would be an asset.