Our team and mission

HRE-SU (Utilisation and Enabled Science) team at ESA coordinates the Agency’s research programme in physical sciences and biology under microgravity conditions and for space exploration. Using platforms like the International Space Station, as well as Moon and Mars missions, the team supports a wide range of cutting-edge research—from Earth atmospheric studies and quantum sensors to heat transfer phenomena, soft matter, and advanced materials processing.

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

Field(s) of activity for the internship

Topic of the internship: Identifying an optimal, unified standard for the use of machine learning software and libraries within the field of microgravity research.

In this internship, you will help to resolve the issue of a lack of standardisation within the use of machine learning within the European Space Agency’s Utilisation and Enabled Science team. Currently, projects utilise a wide variety of different machine learning libraries and software packages, without any coordination between staff members or a consideration of what is optimal or state-of-the-art. As the field of machine learning rapidly advances, this lack of a common standard may lead to difficulties in terms of future inter-staff cooperation or with regards to newer staff members using legacy solutions. Indeed, this issue is very likely to grow over time as the broader field advances and specializes, making the establishment of a common reference point sooner-rather-than-later an even more critical requirement. 

Therefore, your goal as an intern will be to conduct a mixture of literature reviews, software testing and team-member interviews in order to produce a report identifying which modern machine learning software package, if any, should be taken forward as the new standard within the European Space Agency’s Utilisation and Enabled Science team. This will involve identifying not only what libraries are increasing in popularity and feature-set but also those that allow for topic specific architectural alterations and customisations to be made. Considerations such as ‘which libraries are already used by the team’, ‘which are the easiest to transfer to’ and ‘which balance general capabilities with specialist features’ will all need to be balanced when making a final recommendation.

Your final, deliverable report will ultimately be disseminated amongst the team and will act as the foundation from which the emerging field of machine learning will be incorporated into future microgravity research activities. Your work will be the starting point from which new solutions are developed, ensuring that your contribution will play an important role in European Space Agency’s microgravity efforts for many years to come.

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 studying 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.

Knowledge and background in the following is an asset:

• machine learning;
• either physical science or biology.