Intern in the Systems Department - AI and Data Science Section

European Space Agency; Noordwijk; Vor Ort; EUR 13.000 - 20.000; Vollzeit

Zusammenfassung

European Space Agency in Noordwijk, NL invites university students to join the Systems Department AI and Data Science Section as an Intern. You will explore topics ranging from knowledge-graph powered workflows to LLM-assisted code generation, with a focus on space applications and trustworthy AI.

Applicants must be enrolled at a university for the full duration, have proficiency in English (French is an asset), and be prepared to contribute to innovative research under supervision.

Qualifikationen

• You must be a university student, preferably in your final or second-to-last year of your degree.
• Working languages of the Agency are English and French; a good knowledge of one is required.
• Knowledge of Large Language Models and agentic systems is required; familiarity with Knowledge Graphs is an asset.

Jobbeschreibung

Intern in the Systems Department - AI and Data Science Section

Job Requisition ID: 21053

Date Posted: 1 November 2026

Closing Date: 30 November 2026 23:59 CET/CEST

Directorate: Technology, Engineering and Quality

Workplace: Noordwijk, NL

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 programme directorates) is responsible for developing and providing the right Engineering Tools, Methods and Standards:

• leading the Agency's 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 interfaces, support for science operation centres and downstream applications;
• supporting projects with the centre of competence in Sustainability Engineering for all areas related to Clean Space, Life Cycle Analysis, EcoDesign, Zero Debris, REACH and RoHS;
• performing pre-Phase A mission and system design studies in support of programmes' preparation of future missions, providing cost assessments and developing next-generation methodologies including Concurrent Design Facility (CDF), MBSE, and associated ontologies;
• 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 ESA AI Competence Centre and lead the Agency's centre of excellence for advanced technologies supporting the 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 and the provision of innovative and enabling technologies, processes, methods, tools and applications. The AI and Data Science Section carries out technology research in the field of Artificial Intelligence, Data Science and related areas.

You can choose among the following topics:

1) Topic 1: Knowledge Graph-powered Agentic workflows for Space Applications

This internship first explores methods for combining knowledge graphs with agentic workflows (e.g., graph-as-memory, Agentic Graph RAG, or graph-as-coordination-hub for multi-agent systems) and maps these methods against candidate use cases in the space domain. Building on this exploration, you will then design, develop and evaluate a prototype implementing one selected use case, demonstrating how the chosen method addresses a concrete space-related challenge.

2) Topic 2: Synthesis of Pareto-Optimal Interpretations for Neural Network-Based Anomaly Detection Systems

This internship explores the synthesis of Pareto-optimal interpretations for neural network-based anomaly detection systems intended for autonomous onboard satellite operations. The objective is to investigate methods for generating simple, human-understandable representations (such as decision diagrams) of complex black-box models (such as neural networks), while balancing competing objectives including predictive fidelity, interpretability, and representation complexity. Constructing transparent and trustworthy interpretations is a key requirement for machine learning components in mission-critical domains, where accountability and explainability are essential. You will tailor state-of-the-art symbolic reasoning techniques to machine learning models relevant to ESA programmes, with a particular focus on neural network-based onboard anomaly detection.

3) Topic 3: LLM-Assisted Verified Code Generation

The increasing complexity of safety-critical software for space systems calls for new development methodologies that combine automation with rigorous correctness guarantees. This internship investigates the role of Large Language Models (LLMs) as assistants in the development of formally verified software components for critical systems. You will explore LLM-assisted formal specification, where natural-language system requirements are translated into precise formal properties. Building on these specifications, the internship will study approaches for automated code generation and refinement, using formal verification techniques to validate correctness, safety, and reliability. The objective is to move beyond treating LLMs as autonomous code generators and instead establish a workflow in which LLMs serve as efficient engineering assistants whose outputs are systematically checked and validated through formal methods.

4) 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.

6) Topic 6: Evaluation Methods for Agentic AI in Systems Engineering

Agentic AI systems are increasingly capable of performing complex engineering tasks involving information retrieval, reasoning and the use of external tools. However, systematically evaluating their correctness, consistency and robustness remains challenging, particularly for tasks where results must be supported by different engineering sources.
In this internship, you will investigate evaluation methods for agentic AI applied to systems engineering tasks. You will define representative tasks and evaluation criteria, and develop a reproducible framework for comparing different agent configurations, models and reasoning strategies.
The evaluation will consider representative activities such as requirements analysis, traceability assessment and verification support.
The outcome of the project will be a reusable methodology for assessing the capabilities and limitations of agentic AI in engineering applications, with the aim of supporting engineering activities and project reviews.

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

You must be a university student, preferably in your final or second-to-last year of university course 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: Knowledge Graph-powered Agentic Workflows for Space Applications

• knowledge of Large Language Models and agentic systems is required;
• familiarity with Knowledge Graphs is considered an asset.

Topic 2: Synthesis of Pareto-Optimal Interpretations for Neural Network-Based Anomaly Detection Systems

• basic knowledge of machine learning models is required, particularly neural networks and decision trees;
• an interest in explainable AI and familiarity with automated reasoning techniques (e.g., SAT and MaxSAT solving) are considered assets.

Topic 3: LLM-Assisted Verified Code Generation

• familiarity with Large Language Models and agentic systems is required, along with a strong background in software design and programming;
• experience with formal methods and automated theorem provers, especially the LEAN proof assistant, is considered an asset.

Topic 4: Symbolic AI to Simulate Quantum Convolutional Neural Network for Satellite Data Classification

• knowledge of 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.
• familiarity with Large Language Models and their agentic systems is required;
• experience working with robotics libraries (ROS/ROS2) is considered an asset.

Topic 6: Evaluation Methods for Agentic AI in Systems Engineering

• familiarity with Large Language Models and agentic systems is required, along with good programming skills, particularly in Python;
• an interest in systems engineering and experience with evaluating AI systems, information retrieval or automated reasoning are considered assets.

Diversity, Equity and Inclusiveness
ESA is an equal opportunity employer, committed to achieving diversity within the workforce and creating an inclusive working environment. We therefore welcome applications from all qualified candidates irrespective of gender, sexual orientation, ethnicity, religious beliefs, age, disability or other characteristics.

At the Agency we value diversity, and we welcome people with disabilities. Whenever possible, we seek to accommodate individuals with disabilities by providing the necessary support at the workplace. The Human Resources Department can also provide assistance during the recruitment process. If you would like to discuss this further, please contact us via email at [e-mail] .

Important Information and Disclaimer
During the recruitment process, the Agency may request applicants to undergo selection tests.

Applicants must be eligible to access information, technology, and hardware which is subject to European or US export control and sanctions regulations.

The information published on ESA's careers website regarding internship conditions is correct at the time of publication. It is not intended to be exhaustive and may not address all questions you would have.

Nationality

Please note that applications are only considered from nationals of one of the following States: Austria, Belgium, Cyprus, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, the Netherlands, Norway, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland, and the United Kingdom.