Intern in the Systems Department - AI and Data Science Section - European Space Agency (Noordwijk)

About this position

The summary below is published by European Space Agency on the official vacancy notice. Our own analysis — salary realism, comparable openings, career trajectory, language profile — follows further down the page.

Key responsibilities

According to the vacancy notice, the role centres on the following duties:

• Explore methods for combining knowledge graphs with agentic workflows for space applications (Topic 1)
• Design, develop and evaluate prototypes implementing selected use cases (Topic 1)
• Investigate methods for generating human-understandable representations of complex black-box models (Topic 2)
• Tailor symbolic reasoning techniques to machine learning models for onboard anomaly detection (Topic 2)
• Explore LLM-assisted formal specification and automated code generation (Topic 3)
• Integrate quantum-circuit simulators into QML pipeline for satellite image classification (Topic 4)
• Implement agentic LLM model framework for rover autonomy using ROS/ROS2 (Topic 5)
• Investigate evaluation methods for agentic AI applied to systems engineering tasks (Topic 6)

Are you eligible?

Check these requirements from the notice before investing time in an application:

• Education: University student, preferably in final or second-to-last year of Master's level course; must be able to prove enrollment for entire internship duration
• Languages: English (good), French (good)
• Key skills: Knowledge of Large Language Models and agentic systems (Topic 1), Familiarity with Knowledge Graphs (Topic 1, asset), Basic knowledge of machine learning models, particularly neural networks and decision trees (Topic 2), Interest in explainable AI (Topic 2, asset), Familiarity with automated reasoning techniques such as SAT and MaxSAT solving (Topic 2, asset), Strong background in software design and programming (Topic 3), Familiarity with Large Language Models and agentic systems (Topic 3), Experience with formal methods and automated theorem provers, especially LEAN proof assistant (Topic 3, asset), Knowledge of quantum machine learning and quantum circuits (Topic 4), Experience with ROS/ROS2 library (Topic 5)
• Eligibility: Must be a university student; knowledge of ESA Member State language is an asset

Contract and working arrangements

Contract duration: Internship (specific duration not stated).

Working arrangements: ESTEC, Noordwijk, Netherlands.

Hiring unit: Systems Department, AI and Data Science Section, Future Engineering Division, Technology, Engineering and Quality Direc….

Position details

Reference

1452886633

Last Verified

9 November 2026

Position overview

This is the official EU Careers listing for Intern in the Systems Department - AI and Data Science Section at ESA based in Noordwijk (Traineeship). The vacancy reference is 1452886633.

Job Requisition ID:21053

Date Posted:1 November 2026

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

Publication Method:External Only

Directorate:Technology, Engineering and Quality

Location ESTEC, Noordwijk, Netherlands

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.

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

Field(s) of activity for the internship

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.

5) Topic 5: Agentic-LLM ROS2 Controller for Rover Autonomy

Increased autonomy of rovers would allow for further scientific exploration. This internship explores the potential role agentic Large Language Models could play in enabling this autonomy, as well as its potential limitations. In this internship, you will implement an agentic LLM model framework that can take as input a task list and subsequently issue commands to the rover (using the ROS/ROS2 library) to accomplish these tasks and test the resulting quality of this framework. The internship objectives include integrating pathfinding and collision avoidance tools and models into this agent; deploying this agent both in a lunar simulation environment and on board an analogue rover for performance evaluation; and exploring which stopgaps to implement to minimise machine error.

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.

Behavioural competencies

Education

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.

Application timeline

This vacancy was first listed on 9 November 2026, today.

The application deadline is 30 November 2026, 20 days from today. Late applications are not accepted, so submit through the official EU Careers portal well in advance.

Last verified against the EU Careers feed on 9 November 2026.

Where to learn more

For headcount, mission, and other open vacancies at ESA see the ESA institution page; for the cost of living, correction coefficient, and other postings in Noordwijk see our Noordwijk location page.

New to EU careers? Our beginner's guide walks through entry routes, EPSO competitions, and what to prepare. For application logistics see application tips and EPSO competitions.