Our team and mission

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.

Further information on PANGAEA and CAVES programmes can be found on the following websites:

ESA - What is PANGAEA?

ESA - What is CAVES?

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

Field(s) of activity for the internship

You can choose between the following topics:

1) Topic 1: Machine Learning for recognition of planetary materials from multispectral datasets

You will contribute to the ongoing development of Machine Learning algorithms for recognition of planetary materials from multispectral datasets. This project combines deep learning multi-class and multi-label classification algorithms together with data fusion from multi-method spectroscopy, allowing real-time mineral and rock recognition.

For detailed information on this internship position, please click here: ESA - Space training team – Planetary Mineral Database Development and Validation of Spectra Classification Methods

2) Topic 2: Development of Analytical Toolset for recognition of Planetary Materials and Validation of Spectra

You will contribute to the ongoing enhancement and development of the PANGAEA analytical toolset. Working alongside Machine Learning developers, interns will collect, organise, and evaluate analytical data to ensure training datasets meet scientific standards, while offering subject matter expertise to guide ML-based algorithms development. Key responsibilities include expanding, curating and quality-assessing multi-spectral archives within the Mineralogical Database, with a focus on its analytical reference library, validation mineral samples, and petrographic information catalogue of planetary analogue minerals and rocks. You will also assist in validating spectral identification methods through expert review of ML outputs. Additional opportunities may include supporting PANGAEA training course development.

For detailed information on this internship position, please click here: ESA - Space training team – Planetary Mineral Database Development and Validation of Spectra Classification Methods 

3) Topic 3: 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. You will support the software or hardware development of this exploration tool to improve, extend and test its functionalities.

For detailed information on this internship position, please click here: ESA - Space training team – Electronic Fieldbook (EFB) development 

4) Topic 4: Astronaut Training Database (ATD) development

You will support the definition and implementation of the ATD technology evolution roadmap, involving the update of ATD software, hardware, and infrastructure required to adopt emerging technologies, changing business needs, and security constraints. You will also help to identify areas of improvement across the entire ATD system, especially on security aspects/vulnerabilities, and support the implementation of existing vulnerability remediation plans. In addition, you will contribute to the development of an ATD mobile hybrid application.

For detailed information on this internship position, please click here: ESA - Space training team – Astronaut Training Database (ATD) software development support 

5) Topic 5: Legal framework for astronaut field training

You will support the analysis of the relevant legal, safety and insurance frameworks around the ESA CAVES and PANGAEA programmes (mainly Administrative Law, Environmental Law, Occupational Law, Insurance Law). The objective is to produce a detailed summary of the applicable rules and regulations for both programmes, taking into account their practical implications and potential challenges. These programmes operate in regulated and sensitive environments, involve risky activities and personnel from multiple agencies and companies. This requires careful legal and insurance planning, as well compliance to occupational regulations. Differences in national laws, environmental protection, property rights and other considerations create a multifaceted legal, safety and insurance context in which to conduct complex field activities in a safe, legal and appropriately risk managed way. These challenges are a critical, though often invisible, aspect of enabling high-quality astronaut training in analogue environments.

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.

The topics respectively require the following educational knowledge:

Topic 1:

You should possess practical experience in classification techniques, particularly those based on Machine Learning supervised classification. Relevant experience includes, but is not limited to, data mining, data fusion, statistics, clustering, signal decomposition/unmixing, incremental few-shots learning, or other alternative classification methodologies. Experience in processing and analyzing data derived from analytical instrumentation, or working with databases, is also valuable. Academic or professional proficiency with the programming languages and frameworks currently used in the project is required, specifically Python, TensorFlow, Keras, Scikit-learn, Numpy, and matplotlib. Additional experience with Jupyter notebooks and the analysis and visualisation of scientific data would be considered a plus. Experience in integrating MLOps software and datasets together will be considered a valuable asset.

Topic 2:

It would be beneficial for candidates to possess a good understanding and practical experience in utilising, processing, and analysing data from analytical methods and instrumentation. This is particularly relevant for molecular and atomic spectroscopy techniques such as Visible and Near-Infrared (VNIR), Raman, Laser-Induced Breakdown Spectroscopy (LIBS), and X-ray Fluorescence (XRF). Experience in working with and maintaining digital information catalogues and databases would also be advantageous.

Familiarisation with geochemistry, mineralogy and petrology, or attendance of courses related to planetary geology, astrobiology or planetology would be considered a valuable asset.

Topic 3:

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.

Topic 4:

An educational background in software engineering or computer sciences is required, with specific skills in:

• Back-end / Front-end programming languages and frameworks used in the ATD project: ASP.Net, .Net Core, HTML, JavaScript, MS Visual Studio;
• Practical experience with database management system and database administration used in the ATD project: MS SQL Server, T-SQL;
• Practical experience in web-mobile hybrid application development;
• Practical experience in IT security would be an asset;
• Practical experience in web application framework software / Front-end web development would be an asset.

Topic 5:

An educational background in Law is required, with specific background and/or experience in:

• Conducting legal research and summarising relevant regulations and instructions;
• Analysing, organising, mapping, synthesising complex legal information, and preparing laymen summary information and instructions;
• Examination of comparative law and identification of relevant discrepancies;
• Understanding, interpreting, organising and analysing complex information;
• Conducting legal research and analytical reasoning;
• Ability to synthesise/summarise complex information;
• Knowledge of Spanish, Italian and/or German language;
• Knowledge of Spanish, Italian and/or German law will be an asset;
• Knowledge of occupational safety legislation will be an asset;
• Knowledge of speleology and/or environmental legislation will be an asset.