Our team and mission

The Systems Department is responsible for Systems Engineering, its implementation within Projects and In Orbit Demonstration and the innovative solutions - in terms of technology and methodology - enhancing its effectiveness: Artificial Intelligence, Advanced Simulation, Augmented Reality, Concurrent and Collaborative Engineering, Cost Engineering, Digital Engineering, MBSE, Sustainability. Standardisation is also within the responsibility of the Systems Department, ensuring that processes are formalised and shared cross the broad community of space stakeholders.

The Future Engineering Division (in support of the program directorates) is responsible for developing and providing the right Engineering Tools, Methods and Standards:

• Leading the Agency centre of excellence for Artificial Intelligence and processing technologies for space applications;
• Leading the centre of excellence for Advanced Simulation Methods including scientific data processing and databases, software applications including human computer interface, support for science operation centres and downstream applications;
• Supporting projects with the centre of competence in Sustainability Engineering for all areas related to CleanSpace, Life Cycle Analysis, EcoDesign, ZeroDebris, REACh and RoHS;
• Performing pre-Phase A mission and system design studies in support of programmes’ preparation of future missions, provide cost assessments and develop next generation methodologies including concurrent engineering (CDF), MBSE, and respective ontology;
• Supporting the evolution of (ECSS) standards supported by all TEC departments and in full coordination with programmes.

The purpose of the AI and Data Science Section is to implement the AI Competence Centre and leads the Agency’s centre of excellence for advanced technologies to support development and exploitation of future space missions contributing to the main mission of the Technology, Engineering, and Quality directorate in supporting European projects and their success, through excellent engineering, the provision of innovative and enabling technologies, process, methods, tools and applications. The AI and Data Science Section carries out technology research in the field of Artificial Intelligence and Data Science and related areas.

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

Field(s) of activity for the internship

You can choose between the following topics: 

Topic 1:

Sustainability of AI Systems
Life Cycle Assessment (LCA) is a method used to evaluate the environmental impact of a product, process, or service throughout its entire life cycle. LCA is a core part of ESA's environmental strategy and is required for all upcoming ESA missions. Given that AI systems are anticipated to become a standard part of future space missions, it is essential to incorporate them into LCA measurements. To do so, we have to better understand their ecological impact in the context of space systems.
During this internship, you will tailor and apply LCA methodologies to evaluate the environmental impact of AI systems within the context of space missions. You will also suggest sustainable practices to reduce the ecological footprint of these technologies (i.e., Frugal AI). This project offers the unique opportunity to contribute meaningfully to the responsible and sustainable integration of AI into future space systems.

Topic 2:

Embodied AI: A Brain for Our Rover
Help shape the future of autonomous space exploration at ESA! Embodied AI refers to AI systems which have a physical or virtual body enabling them to perceive and interact with the world. Such system combines perception, action, and reasoning to operate in dynamic, real-world environments.
In this internship, you will have the opportunity to develop the autonomous decision-making and navigation capabilities of a small rover using Large Language Model (LLM)-based agentic workflows. Your work will contribute to building intelligent, adaptive systems that can operate independently in remote and extreme environments, paving the way for the next-generation exploration missions.

Topic 3:

Scientific Trend Analysis via Machine Learning on Evolving Knowledge Graphs
ESA is confronted with a growing body of research ideas, directions, and domains in its research and development programs. This growing corpus of scientific proposals contains valuable information about current and emerging trends in research and technology. In this internship, you will apply state-of-the-art text-mining tools to extract scientific concepts from this corpus. These concepts will be organised in a time-evolving knowledge graph, which connects concepts that appear jointly in activities and tracks the relevance of these connections in ESA's programs. Based on this dynamic knowledge graph, you will then implement a machine learning pipeline that predicts new connections in the network that may become relevant in the future. This project offers you the chance to explore neuro-symbolic architectures while gaining insight into ESA's fascinating research and development activities.

Topic 4:

Symbolic AI to Simulate Quantum Convolutional Neural Network for Satellite Data Classification
Quantum machine learning (QML) is emerging as a promising technology in image classification and remote sensing tasks that naturally arise in Earth observation. However, due to current hardware limitations, the quantum circuit embedded into the AI pipeline is limited in size and the gate basis that it can use. We can still assess the potential advantage of QML approaches using simulators for the quantum circuit. In this internship, you will integrate novel quantum-circuit simulators based on automated reasoning techniques into the QML pipeline to classify satellite images. The automated reasoning approach is agnostic to the gate basis used by the circuit, allowing quantum circuits of different sizes and with different gates to be implemented and evaluated in this context. The outcome of the project will help us prepare for the time when quantum computers are commonly available and provide you with a unique opportunity to apply cutting-edge machine learning and quantum computing technologies to real satellite data.

Topic 5:

Exploring Binarised Neural Networks for Satellite-Based Land Classification
Binarized Neural Networks (BNNs), which constrain both weights and activations to a single bit, offer significant advantages over traditional Deep Neural Networks (DNNs) in terms of memory efficiency, computational speed (by replacing floating-point operations with lightweight 1-bit XNOR-count operations), and the potential for formal verification against quantitative specifications. These features make BNNs particularly appealing for on-board space applications, where resources are limited and reliability is key. In this internship, you will investigate the performance of BNNs compared to conventional DNNs and quantized DNNs (e.g., at 8-bit precision) using real satellite data for land use and land cover classification. A unique opportunity to explore innovative AI architectures in the context of real-world Earth observation challenges.

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 must be a university student, preferably studying at master’s level. In addition, you must be able to prove that you will be enrolled at your University for the entire duration of the internship.

Additional requirements

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

During the interview, your motivation for applying to this role will be explored.

You should also have:

Topic 1: Sustainability of AI Systems
Experience in LCA/sustainable development methods or AI systems is required. Knowledge of AI applications in the space field is an asset.

Topic 2: Embodied AI: A Brain for Our Rover
Experience in AI applications and embedded AI including LLMs and agentic workflows is required. Experience with robotic systems is an asset.

Topic 3: Scientific Trend Analysis via Machine Learning on Evolving Knowledge Graphs
Experience in setting up AI applications in Python is required, e.g., with TensorFlow or Keras. A basic understanding of text-mining procedures will be helpful as well, and first experience with knowledge graphs is an asset.

Topic 4: Symbolic AI to Simulate Quantum Convolutional Neural Network for Satellite Data Classification
Knowledge in gate-based quantum computing and experience in developing AI pipelines in Python are required. A basic understanding of propositional logic and symbolic AI is an asset

Topic 5: Exploring Binarised Neural Networks for Satellite-Based Land Classification
Applicants should have a solid foundation in machine learning, with experience in neural network architectures and Python-based frameworks such as PyTorch or TensorFlow.