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 also cooperate with scientists and engineers from EOP-FA (the System Architect Office) at ESTEC, and potentially with staff in other parts of ESA.

You will be part of the ESA Φ-lab, whose 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 innovations such as AI, machine learning, quantum computing, edge computing, metamaterials and photonics. Many of the challenges posed by new technologies need to be tackled at scientific, application and capability levels to deliver the maximum value from satellite-derived EO assets for our climate, society and economy. The Φ-lab brings together early career and senior researchers from a variety of disciplines across EO in pursuit of disruptive/transformative innovation to contribute to the development of novel 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, mentor members of our research network and engage in outreach activities, all generally but not exclusively related to your research topic. 

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

Field(s) of activity/research for the traineeship

The objective of this research fellowship is to advance intelligent, agentic AI-driven, goal-oriented mission planning for EO satellite constellations, with a strong focus on autonomy, responsiveness, and coordinated execution across multiple space assets. The research will centre on the design and implementation of onboard AI systems that enable the autonomous management of mission planning, data acquisition, and satellite tasking in real time.

The fellowship will investigate how mission planning can evolve from static scheduling to an adaptive, intelligence-driven process executed directly on board satellites. This includes the development of onboard AI systems capable of continuously monitoring payload data, platform status, and environmental conditions, and autonomously updating mission plans accordingly. These systems will combine AI-based decision-making, learning approaches, and predictive capabilities to enable spacecraft to react proactively to events while reducing reliance on ground intervention.

A key focus will be the autonomous coordination of multi-satellite constellations through onboard intelligence. The typical scenario this research will address is how, once an event is detected by a “tip” satellite, onboard AI systems can coordinate “cue” satellites by determining the optimal response: identifying which satellites should be tasked, when observations should occur, and which asset can capture the event first. This requires distributed onboard AI capabilities for task allocation, resource management, and real-time constellation coordination.

The fellowship will also address the design of onboard and system-level orchestration architectures, investigating centralised, distributed, or hybrid approaches for coordinating autonomous satellite operations. The research will explore how onboard AI can enable scalable, resilient and adaptive constellation management through intelligent decision making and cooperation among space assets.

While not the primary focus, the research may also explore additional onboard functionalities that can further improve operational performance, such as the integration of predictive models, orbital dynamics knowledge, or drag-aware optimisation strategies to enhance manoeuvre timing and mission responsiveness.

Building on this, the fellowship will also explore applications in fragile or complex environments, including resilience and dual-use scenarios, where the AI system will process EO and contextual data to anticipate climate-related and security-relevant risks, and suggest context-aware, goal-driven responses supporting both civilian and institutional operational needs.

Ultimately, the research aims to enable a new paradigm of EO operations in which onboard AI systems form the core intelligence layer of mission planning, continuously integrating data, predictions and system knowledge to drive autonomous, coordinated and efficient satellite operations.

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Main Research Topics

• Onboard Agentic AI for Mission Planning and Autonomous Management
  Develop onboard AI systems possibly based on agentic AI for autonomous mission planning, data acquisition and satellite tasking, adapting observation strategies based on real-time inputs and priorities.
• Smart Monitoring and AI-Based Control
  Design intelligent frameworks to monitor EO data, spacecraft status, and contextual information, enabling autonomous event detection, replanning and optimisation.
• Distributed Onboard AI for Multi-Satellite Orchestration
  Develop architectures for autonomous satellite coordination, including centralised, distributed, and hybrid orchestrator approaches for scalable constellation management, including tip-and-cue operations, enabling rapid optimised event response.
• Advanced Onboard Functionalities for Operational Enhancement
  Explore additional onboard capabilities, including predictive models, adaptive optimisation, and optional integration of orbital dynamics considerations.
• Simulation, Learning and Operational Validation
  Develop AI-enabled simulation environments and testbeds to validate autonomous mission planning under realistic operational constraints.

Within your application, please provide a research proposal (no more than 5 pages) answering the following questions:

• Describe your main research questions and how you would like to address them.
• What technological innovations in onboard AI will overcome current EO system limitations and enhance autonomous decision making, operational efficiency and responsiveness?
• How can AI-driven EO systems adapt in real time to uncertain, fragmented, or high-risk environments, including those with limited ground-truth data?
• What role will predictive models and real-time environmental signals play in improving the accuracy and value of EO-based actions?
• What are the main technical and operational challenges in developing autonomous EO frameworks, and how can safeguards be embedded to address both humanitarian and security needs?
• How will this research shape the long-term sustainability, adaptability and strategic value of EO in future space missions and global climate response?

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In particular, you will:

• undertake advanced research activities exploring and expanding the use of disruptive and transformative innovation such as AI, machine learning, quantum computing and edge computing to develop new frameworks and solutions. Research may cover a wide range of innovative topics:
  • exploring innovative methodologies and technologies;
  • the development of novel methods, of new technology implementations (e.g. ML models) and of high-level products;
  • contributing 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, and visiting professors, experts and researchers, as applicable;
• design and develop onboard AI systems that autonomously optimise EO systems tasking and data collection, using decision intelligence to make real-time decisions based on environmental signals and predictive models;
• contribute to the rapid prototyping and testing of AI-driven Earth observation systems, with a focus on the autonomous, decision-making capabilities that enable satellites to adapt to dynamic environmental changes and urgent events;
• collaborate closely with ESA’s Φ-lab and internal teams to integrate onboard AI and decision intelligence.

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, or be close to completion of a PhD in a related technical or scientific discipline. Preference  will  be  given  to applications submitted by candidates within  five  years of receiving their PhD. In particular for this position, the following is required:

PhD in data science, AI, computer science, machine learning, aerospace engineering, Earth system science or climate, etc. 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, satellite systems, mission planning, ML, agentic AI and edge computing;
• 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;
• 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.