Location

ESTEC, Noordwijk, Netherlands 

Our team and mission

The Galileo Ground Segment is managed by the Galileo Ground Segment Management Office (NAV-PT), which falls under the Galileo and EGNOS Programme Department. The team responsible for the Ground Segment focuses on managing infrastructure and services related to various components of the Galileo system, including the Mission Segment, Ground Control Segment, and Security Monitoring. The office ensures the system's design, validation, and operational capabilities meet the required technical and security standards, and closely collaborates with other segments such as the Space Segment Management Office and support offices like the Navigation Product Assurance and Safety Office.
The team works in coordination to manage the deployment, risk management, and cost control of the Ground Segment, focusing on continuous improvement and addressing any anomalies during operations. The mission includes overseeing the design, deployment, and maintenance of the infrastructure essential for the Galileo Ground Segment, ensuring compliance with security standards and programmatic requirements.

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

Field(s) of activity/research for the traineeship

As a graduate trainee, you will focus on the application of Machine Learning (ML) and advanced data analytics to address predictive maintenance and anomaly detection challenges within the Galileo Ground Segment (GS) network. This role provides an exciting opportunity to contribute to ensuring the reliability and performance of critical systems used in space operations.

Key Activities and Responsibilities:

1. Data Collection and Preprocessing:

• Work with historical telemetry datasets from GS elements, which may include sensor logs, operational metrics, and performance data.
• Clean and preprocess these datasets to ensure their suitability for ML applications, addressing challenges such as missing values, outliers, and irregular time intervals.
• Engineer features to capture meaningful patterns and trends that correlate with hardware health and signal performance.

      2. Machine Learning Model Development:

• Train ML models to predict hardware failures and signal degradations based on telemetry data.
• Utilize open-source frameworks like Darts (for time-series forecasting) to experiment with models such as ARIMA, Exponential Smoothing, and Neural Network-based techniques like Long Short-Term Memory networks (LSTMs).
• Optimize model performance using grid search and cross-validation to ensure reliability and accuracy.

     3.  Anomaly Detection Mechanisms:

• Design unsupervised learning models (e.g., Isolation Forests, Autoencoders) to detect unusual patterns and anomalies in real-time data streams.
• Define dynamic thresholds to minimize false positives and adapt to system variability.

     4. Real-Time Monitoring and Automated Alerting:

• Develop and implement a real-time monitoring pipeline for continuous system telemetry analysis.
• Configure automated alerting mechanisms that notify relevant teams when anomalies or potential failures are detected, ensuring timely interventions.

     5. Integration into Maintenance Workflows:

• Collaborate with operations and engineering teams to integrate predictive insights and anomaly alerts into the existing maintenance scheduling process.
• Propose strategies for resource optimization, including prioritizing maintenance tasks based on predictive analytics and anomaly severity.

      6. Feedback and Continuous Improvement:

• Use maintenance outcomes and operational feedback to retrain and refine ML models, ensuring they evolve with changing system conditions.
• Document findings, challenges, and recommendations for improving ML-based predictive maintenance strategies.

Learning Objectives. By the end of the traineeship, you will:

• Gain advanced knowledge of ML techniques for time-series and anomaly detection in an applied context.
• Understand the operational intricacies of Galileo’s GS network, including hardware performance and signal integrity.
• Develop problem-solving skills by designing scalable solutions to complex predictive maintenance challenges.
• Enhance technical communication by collaborating with multidisciplinary teams and documenting technical findings.

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 just completed, or be in the final year of your master’ s degree in Computer Science or equivalent.

Additional requirements

• Strong analytical skills and familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch).
• Proficiency in Python and experience working with time-series or telemetry data.
• Understanding of signal processing and hardware systems is an asset.
• Excellent communication skills and the ability to work in a multicultural environment.
• Knowledge of the space sector and ESA’s activities is desirable.

You should have good interpersonal and communication skills and should be able to work in a multicultural environment, both independently and as part of a team. Previous experience of working in international teams can be considered an asset. Your motivation, overall professional perspective and career goals will also be explored during the later stages of the selection process. 

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, beliefs, age, disability or other characteristics. Applications from women are encouraged.

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 contact.human.resources@esa.int.
 

Important Information and Disclaimer

During the recruitment process, the Agency may request applicants to undergo selection tests. Additionally, successful candidates will need to undergo basic screening before appointment, which will be conducted by an external background screening service, in compliance with the European Space Agency's security procedures.

The information published on ESA’s careers website regarding working 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 and Languages 
Please note that applications can only be considered from nationals of one of the following States: Austria, Belgium, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Luxembourg, the Netherlands, Norway, Poland, Portugal, Romania, Slovenia, Spain, Sweden, Switzerland, and the United Kingdom. Nationals from Latvia, Lithuania and Slovakia  as Associate Member States, or Canada as a Cooperating State, can apply as well as those from Bulgaria, Croatia, Cyprus and Malta as European Cooperating States (ECS).

According to the ESA Convention, the recruitment of staff must take into account an adequate distribution of posts among nationals of the ESA Member States*. When short-listing for an interview, priority will be given to external candidates from under-represented Member States*. 

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.  

*Member States, Associate Members or Cooperating States.