Intern in the AI, Data Foundation and Digital Twins domains

Our team and mission

This position is based at the European Space Operations Centre (ESOC) - Darmstadt, Germany

The Ground Systems Engineering and Innovation Department is responsible for all the ground systems engineering and support activities required to support mission operations and implement innovation. 
The Department maintains and manages a full level of competences, technologies and services in all engineering disciplines related to ground systems, infrastructure, technology evolution and Engineering R&T and innovation. 
The Department provides expert support in these areas to the Directorate of Operations for all current and potential missions and leads, in the role of architect, ground systems evolution and innovation implementation in the infrastructure and to other ESA programs. 
The Department is furthermore responsible for the overall technical innovation and technical standardisation coordination for the ground segment. 

The Department is relying on the following domains of expertise: 

• Ground Station Engineering
• Flight Dynamics and Mission Analysis
• Mission Operations Data Systems Engineering
• Navigation Support
• Multi-Mission Infrastructure – Ground Segment Integration and Testing

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

http://www.esa.int/Our_Activities/Operations/gse/Building_infrastructure_on_Earth_to_support_satellites_in_space.

Field(s) of activity for the internship

You can choose between the following topics:

1) Topic 1: Data Foundations and Structures 

Spacecraft and their associated ground data systems generate vast amounts of data during the monitoring and control process. This data is stored, processed, and analysed within the mission operations ground segment to ensure spacecraft health, correlate events, and support the development of artificial intelligence (AI) algorithms.
In this topic, you will explore the Agency's data foundations and structures, focusing on their role in advanced mission operations. This includes how data is ingested, managed, and curated, with emphasis on data governance, quality, lineage, accessibility, and compliance to enable data democratization. You can focus on the preparation of AI-ready datasets from existing operational or synthetically generated data, the verification and validation processes of AI, enhance AI model explainability, or address security challenges such as adversarial attacks and data poisoning. Overall, you will design and implement software to expand capabilities and resolve operational issues related to the Data Foundation of a Mission Operations Ground Segment.
Candidates are invited to express their interest in specific areas of the topic.

2) Topic 2: Artificial Intelligence for Space Operations

Artificial Intelligence (AI) is becoming a key technology in modern spacecraft operations applications. It is highly relevant to support automation, correlate data and execute pattern recognition and even enable digital assistants for operators, helping them operate the spacecraft and ground systems, including ground stations. 

As part of this topic, you will contribute to the AI programme of the Directorate. A particular area of interest is this of knowledge engineering and discovery, content generation and reasoning using state-of-the-art technologies like Large Language Models. You will work on the design and implementation of AI software for an operational application, widely used by Flight Control Teams, leveraging the capabilities of generative AI and being exposed to the full lifecycle of an operational AI application.

Another area of interest revolves around time-series analysis, which is essential for identifying patterns and trends over time, particularly within the domain of ground segment engineering and mission operations. Specifically, you will engage in continual learning, developing models that can adapt and improve over time without losing previously acquired knowledge. Additionally, you will explore federated learning, a state-of-the-art approach that facilitates the training of models across decentralized data sources while ensuring data privacy and security. This is especially pertinent to mission operations, where data is often distributed across various locations and space missions. You will have the opportunity to work on real-world challenges, contributing to advancements in AI that can significantly enhance the efficiency and effectiveness of mission operations.

3) Topic 3: Digital Twins, AI Enhanced Simulation Models and Machine Learning 

Simulators are complex software systems used to simulate the behavior of the spacecraft and ground equipment, and are used for testing of the ground segment and the training of the flight control team, as well as integration with operational simulators of other agencies (e.g. in the context of the planned international collaboration in the Moon/Mars programmes). 
Advancements in simulation technologies includes the integration of Digital Twins concepts and the use of Machine Learning algorithms to process signals (time-series) and enhance the modeling of the complex spacecraft systems, predicting their behavior under various operational conditions and helping support and address resolution of operational issue.

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 in your final or second-to-last year of a university course at Master’s level and you need to remain 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 Member State language would be an asset.

Topic 1: Knowledge or academic specialization in data centric architectures and engineering is an asset.

Topic 2: Knowledge or academic specialization in application of software engineering to data science and artificial intelligence solutions is an asset.

Topic 3: Knowledge or academic specialization in application of digital twins, time-series forecasting, machine learning or simulation technology to complex systems is an asset.