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, as well as staff in the Avionics and EEE Division within the Directorate of Technology, Engineering and Quality. You may also collaborate with scientists and engineers in other divisions of ESA.

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 serve 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, including 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 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, 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: https://www.esa.int/

Field(s) of activity/research for the traineeship

Main Objectives of the Research Fellow Activity:

Cognitive Cloud Computing in Space (3CS) refers to the integration of AI, novel computing paradigms, such as cloud computing and edge computing, computing hardware and other technologies on board space systems. By increasing satellite systems’ intelligence and enabling cooperation among different space assets, 3CS has the potential to enable novel mission paradigms, such as space-based data centres, and novel observation strategies, for example virtual constellations, and significantly enhance space’s infrastructure autonomy, responsiveness and efficiency. Such novel capabilities can directly benefit multiple EO applications such as disaster monitoring and emergency response, maritime and land surveillance.

The research activity aims to build efficient data handling processing chains for EO 3CS missions, with a focus on hardware and architectural elements and prototyping. In particular, it will analyse 3CS-enabled novel mission paradigms and observation strategies, derive their main requirements, and investigate the design of the most efficient data processing chains and the most suitable hardware elements. Moreover, it will investigate potential advantages brought about by the use of innovative computing paradigms in the implementation of such processing chains.

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Potential Research Topics

This fellowship will focus on at least one of these research areas:

• Efficient data handling processing chains: design of onboard data handling processing architectures for 3CS-enabled missions for EO. This includes exploring hardware and software solutions for the inference of complex AI models, such as visual language models, onboard data fusion, AI model training, and investigating architectures like mass-memory-cantered and online processing and benchmarking of promising hardware devices based on metrics such as energy efficiency, performance and radiation tolerance.
• Standardisation of hardware-software interfaces: defining modular, standardised interfaces for seamless integration of emerging hardware accelerators into existing space processing pipelines.
• Hardware-aware and trustworthy training pipelines for edge AI: developing edge AI models for EO systems with a focus on:
  • hardware-aware efficiency: utilising hardware-in-the-loop training and neural architectural search for optimised onboard performance;
  • trust and compliance: design for mission-critical systems with, for example, explainability and adherence to the European AI Act through transparent, risk-aware model design.
• Disruptive computing paradigms for onboard data processing: designing, prototyping and benchmarking onboard data processing chains at both hardware and software levels, based on disruptive computing paradigms, such as photonic and neuromorphic computing, and corresponding sensing technology, for example event-based cameras.
• Cyber security for onboard AI model robustness: designing robust data processing pipelines for AI-based onboard processing to prevent data manipulation or adversarial attacks.

As part of your application, please provide a research proposal of no more than five pages relating to the development of efficient data handling processing (both hardware and software) for 3CS-enabled EO missions. Your proposal should:

• describe the main research questions that you would like to address;
• propose a research plan outlining, with a clear description, the methodology that you would like to adopt to answer those research questions, with specific reference to:
  • your previous experience in relation to the topic, with clear reference to your publications, and how this could be leveraged for your research plan;
  • the reference EO 3CS mission paradigms, for example space-based data centres and low-latency response systems, novel observation strategies, and the main EO applications upon which you would like to focus. Please highlight the potential benefits of your research at application/mission level;
  • the main hardware devices and computing paradigms for edge computing and learning that you would like to investigate;
  • a possible schedule, including clear expected objectives relating to papers and data sets, for example;
  • any other methodological elements, for example relating to tools and collaboration.

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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. Your research may cover a wide range of innovative topics and include:
  • exploration of innovative methodologies and technologies;
  • the development of novel methods and new technology implementations of high-level products, for example machine learning models;
  • contributions 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 laboratory 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 the research network of ESA graduate trainees, national trainees, interns and visiting professors, experts and researchers, as applicable;
• collaborate closely with ESA’s Φ-lab and Avionics and EEE Division on the identification, testing and benchmarking of hardware components for edge computing and learning;
• conduct research and implement prototypes of 3CS-based efficient processing chains for 3CS missions;
• support ESA’s Φ-lab staff with external R&D activities relating to onboard AI and 3CS.

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 electronics, embedded systems, computer science, AI, machine learning, aerospace engineering, Earth system science or climate, with a thesis subject relevant to the above description of tasks.

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:

• Experience with hardware benchmarking for edge computing (e.g. FPGAs, TPUs, VPUs and GPUs) and edge learning (e.g. GPUs and FPGAs) paradigms;
• Experience in the design of edge computing data processing chains;
• Sound knowledge of computing architectures, such as RISC, CISC and SIMD, VLSI processes, edge AI design flow, embedded systems, machine learning and data science;
• Basic knowledge of onboard AI, 3CS concepts, satellite systems and space missions;
• The ability to think outside the box and explore new avenues, with natural curiosity and a passion for new subjects and research areas;
• Basic knowledge of AI agents and large language models;
• Experience with one or more general-purpose programming languages, for example Python, and general-purpose deep learning frameworks, such as Tensorflow or PyTorch;
• Interest in and ability to share knowledge with other ESA organisational units.

The following elements are considered an asset:

• Knowledge of edge learning paradigms, such as distributed learning and federated learning;
• Basic knowledge of EO data processing chains for optical and/or SAR data (e.g. level 0/level 2);
• Knowledge of communication protocols, for example Ethernet, PCIe, SpaceWire and SpaceFibre;
• Previous experience with edge computing systems based on disruptive computing paradigms, for example neuromorphic computing and photonic computing;
• Previous experience with ASIC/FPGA design;
• Knowledge of AI security at hardware and software levels.

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.