Intern in the CAVES & PANGAEA Team - Machine Learning Development - European Space Agency (Porz-Wahn)

About this position

The summary below is published by European Space Agency on the official vacancy notice. Our own analysis — salary realism, comparable openings, career trajectory, language profile — follows further down the page.

Key responsibilities

According to the vacancy notice, the role centres on the following duties:

• Develop and test ML classifiers for multi-method spectroscopic data, including signal unmixing, multi-modal data fusion, hierarchical classification, incremental few-shot learning, conformal prediction and uncertainty e…
• Build, maintain and optimise the PANGAEA Analytical Toolset software applications
• Evaluate ML outputs against ground-truth multi-spectral MinDB reference archives and petrographic databases, and expand and curate the datasets

Are you eligible?

Check these requirements from the notice before investing time in an application:

• Education: University student, preferably in final or second-to-last year of Master's level course, with proof of enrollment for entire internship duration
• Experience: Practical experience in classification techniques, particularly Machine Learning supervised hierarchical classification and regression. Relevant experience includes Data mining, Data fusion, Statistics, Clustering, Signal decomposition/unmixing, Incremental few-shot learning, Conformal prediction, CNN or KAN ML architectures. Practical experience in processing and analysing data from analytical i…
• Languages: English, French
• Key skills: Python programming, NumPy, JAX, TensorFlow, Keras, CatBoost, Scikit-learn, Accelerating Python code using Cython, JAX or similar performance optimisation tools, Analysis and visualisation of scientific data (Matplotlib), MLOps workflows and dataset integration

Contract and working arrangements

Contract duration: Internship starting throughout 2027.

Working arrangements: EAC, Porz-Wahn, Germany.

Hiring unit: Human and Robotic Exploration Directorate, CAVES and PANGAEA Team.

Position details

Reference

1453000933

Last Verified

2 November 2026

Position overview

This is the official EU Careers listing for Intern in the CAVES & PANGAEA Team - Machine Learning Development at ESA based in Porz-Wahn (Traineeship). The vacancy reference is 1453000933.

Job Requisition ID:21138

Date Posted:1 November 2026

Closing Date:30 November 2026 23:59 CET/CEST

Publication Method:External Only

Directorate:Human and Robotic Exploration

Location EAC, Porz-Wahn, Germany

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.

One such tool developed for PANGAEA and future missions is the PANGAEA Analytical Toolset. This comprises the Mineralogical Database (MinDB), a curated collection of information on all the minerals found on the Moon, Mars and in meteorites, and the Machine Learning (ML) software that combines deep learning multi-class and multi-label classification algorithms analysing fused multi-modal spectroscopic measurements, enabling a significant accuracy increase of mineral and rock recognition.

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

We are looking for internships to start throughout 2027. Candidates are invited to state their preferred starting date in the cover letter.

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, Software Development, Spectroscopic Analysis, Planetary Mineralogical Characterisation​

​​Interns are sought to contribute to the ongoing enhancement, integration and deployment of the PANGAEA analytical toolset. Depending on the primary specialization, the intern will join one of three complementary streams: Machine Learning (ML) Development and Operations, Applied Software Development or Mineralogical & Spectroscopic Data Curation.

​The activities will include:

- ​developing and testing ML classifiers for multi-method spectroscopic data, including signal unmixing, multi-modal data fusion, hierarchical classification, incremental few-shot learning, conformal prediction and uncertainty estimation; - ​building, maintaining and optimising the PANGAEA Analytical Toolset software applications; - ​evaluating ML outputs against ground-truth multi-spectral MinDB reference archives and petrographic databases, and expanding and curating the datasets.

​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

Education

You must be a university student, preferably in your final or second-to-last year of university course 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.

Candidates should possess practical experience in classification techniques, particularly those based on Machine Learning supervised hierarchical classification and regression. Relevant experience includes, but is not limited to, Data mining, Data fusion, Statistics, Clustering, Signal decomposition/unmixing, Incremental few-shot learning, Conformal prediction, CNN or KAN ML architectures. Practical experience in processing and analysing data derived from analytical instrumentation, or working with databases, is also valuable.

Academic or professional proficiency with the Python programming and core scientific computing/ML libraries: NumPy, JAX, TensorFlow, Keras, CatBoost, Scikit-learn.

Additional skills and experience in one or more of the following key areas will be considered a valuable asset:

- accelerating Python code using Cython, JAX or similar performance optimisation tools; - practical experience with analysis and visualisation of scientific data (Matplotlib); - familiarity with MLOps workflows and dataset integration; - for applied ML, experience with modern web application development, user interface design for applications and web frontend integration, and backend services using FastAPI.

Application timeline

This vacancy was first listed on 2 November 2026, today.

The application deadline is 30 November 2026, 27 days from today. Late applications are not accepted, so submit through the official EU Careers portal well in advance.

Last verified against the EU Careers feed on 2 November 2026.

Where to learn more

For headcount, mission, and other open vacancies at ESA see the ESA institution page; for the cost of living, correction coefficient, and other postings in Porz-Wahn see our Porz-Wahn location page.

New to EU careers? Our beginner's guide walks through entry routes, EPSO competitions, and what to prepare. For application logistics see application tips and EPSO competitions.