Our team and mission

This position is based at the European Astronaut Centre (EAC) – Near Cologne, Germany

The CAVES and PANGAEA team specialises in training programmes that equip astronauts and mission developers with scientific, expeditionary and behavioural skills. The group’s primary output is focused around two training programmes, CAVES, a course that uses natural cave systems for expeditionary and human behavioural and performance training, and PANGAEA, a course for geological and astrobiological field training. Complementary to their training goals, these programmes are used as research and development platforms to advance several of ESA’s technological, scientific and operational areas.

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

Field(s) of activity for the internship

Topic of the internship: Machine Learning for recognition of planetary materials from multispectral datasets

You are sought to continue development of machine learning algorithms for recognition of planetary materials from multispectral datasets. This project focuses on combining several mineral characteristics to achieve automatic classification of minerals and rocks.

For detailed information on this internship position, please click here: https://www.esa.int/About_Us/EAC/Space_training_team_Planetary_Mineral_Database_Development_and_Validation_of_Spectra_Classification_Methods.

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.

Practical experience in Machine Learning based classifications (and/or data mining, data fusion, statistics, clustering, decomposition/unmixing, recommender systems or other alternative classification methods), or in the processing and analysis of data coming from analytical instrumentation, or with databases. Academic or professional experience with the programming languages and frameworks currently used in the project: Python, TensorFlow, Keras, Scikit-learn, Numpy, matplotlib. Additional experience in Jupiter notebook, JavaScript, analysis and visualisation of scientific data is considered a plus. Experience in integrating of MLOps software and datasets together will be considered valuable.