Our team and mission

ESA maintains a world-leading Science Programme with missions in heliophysics, planetary science, astrophysics and fundamental physics. Its mission is to 'empower Europe to lead space science'. The Department for Science and Operations hosts the scientists and engineers that oversee the space missions, from study to end of operations. It develops the science operation systems for the missions and operates the missions in space, as well as archiving and curating their data during operations and beyond. Our main objective is to maximise the scientific output of the missions for the benefit of humankind.

The Data Science and Archives team is located within the Science and Operations Department and aims to support innovative data science activities, with a view to increasing the scientific return of ESA’s science missions by enhancing the science data exploitation. In turn, the efficiency of mission science operations is improved. The team is currently developing a platform to support data analytics initiatives for ESA’s space science missions. The platform, ESA Datalabs, focuses on improving collaboration and enabling easy access to data for scientific exploitation. However, many opportunities remain for extending its functionality.

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

Field(s) of activity/research for the traineeship

As the platform is under active development with many ideas for functional extension, a key aspect involves managing continuous integration/continuous delivery (CI/CD) activities to allow for the regular modification of a platform that is already being used by teams from various missions. With a view to enabling the more efficient use of existing hardware and supporting machine learning use cases, the existing setup needs to be extended with Machine Learning Operations (MLOps) functionality. Aa Young Graduate Trainee, you will work on the following:

• Analysis of existing open source frameworks to identify suitable candidates for integration with respect to the current ESA Datalabs architecture;
• Development of the prototype integration of a corresponding framework;
• Testing the viability of hybrid on-premises and cloud deployment;
• Demonstration of functionality at workshops and/or conferences and supporting interested stakeholders in adoption;
• Validation of the functionality by onboarding existing machine learning projects, for example, related to object detection (for instance Kruk et al. 2022, https://arxiv.org/abs/2202.00246).

Successfully integrating MLOps functionality into ESA Datalabs will allow ESA to run machine learning projects at scale and provide data products for exploitation by the scientific community.

Technical competencies

Behavioural competencies

Result Orientation

Operational Efficiency

Fostering Cooperation

Relationship Management

Continuous Improvement

Forward Thinking

Education

You should have just completed or be in the final year of your master’s degree in computer science or similar.

Additional requirements

Familiarity with CI/CD and MLOps concepts and tools and, ideally, familiarity with solution architecture and hybrid cloud environments would be an asset.

You should have good interpersonal and communication skills and should be able to work in a multi-cultural environment, both independently and as part of a team.

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.

During the interview, your motivation and overall professional career goals will also be explored.