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.

For further information visit our web site: http://www.esa.int

Field(s) of activity for the internship

You can choose between the following topics:

1) Topic 1: Electronic Fieldbook (EFB) system development

The EFB is a tool supporting astronaut mission operations with technology for geolocation, cave mapping, scientific data gathering, sensors interaction and wireless data relay to mission control centres. As an intern, you will support the software or hardware development of this exploration tool to improve, extend and test its functionalities.

For more information on this topic, please visit: Space Training Team – Electronic Field Book development

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2) Topic 2: Machine learning for recognition of planetary materials from multispectral datasets

As an intern, you will continue the 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 more information on this topic, please visit: Space Training Team – Planetary Mineral Database Development and Validation of Spectra Classification Methods

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3) Topic 3: Development of analytical toolset for recognition of planetary materials and validation of spectra classification methods

As an intern, you will continue the enhancing and developing PANGAEA analytical toolset consisting of multi-spectral archives and the Mineralogical Database, a curated collection of reference information and samples of planetary analogue minerals and rocks. You will also help in evaluating the most suitable spectral identification method for planetary materials with help of the custom ML classification software; collecting, organizing, and reviewing the analytical data; and may contribute to the preparation of the PANGAEA training course.

For more information on this topic, please visit: 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

Education

You must have student status and be enrolled at university for the entire duration of the internship. You should preferably be in your final or second to last year of a university course at master’s level in a technical or scientific discipline.

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.

Additional Requirements:

1) Topic 1:

Depending on the chosen branch and profile (either software or hardware):

• Software development: academic or professional experience with at least three of the programming languages, frameworks or engines currently used in the project: JavaScript, AngularJS, Java, CouchDB, HTML, Ionic, Cordova, NodeJS. Experience on one or more of the following paradigms would be considered a plus: C++, C#, Qt, Python, REST, TensorFlow, Docker, user interface design. Previous experience in GIS systems would be a plus.
• Hardware, telecommunications, and prototyping: academic or professional experience in UNIX embedded boards or generic electronics (e.g. RaspberryPi, ESP32, Arduino), as well as having basics of OpenWRT, Wi-Fi and of routing and networking. Experience in hardware assembling and CAD 3D design-printing and prototyping. Familiarity with standards for ergonomics in terrestrial applications, and in ECSS for space utilisation would be considered a plus.

2) Topic 2:
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.

3) Topic 3:
Having a good knowledge of and practical experience in using, processing and analysing data of analytical methods and instrumentation, in particular of molecular and atomic spectroscopy (VNIR, Raman, LIBS, XRF), or working with and maintaining digital information catalogues and databases are a benefit.
Familiarisation with geochemistry, mineralogy and petrology, or attendance of courses related to planetary geology, astrobiology or planetology would be an asset.