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Intern in the CAVES & PANGAEA Team, Machine Learning Development

European Space Agency -
Wahn

Jetzt bewerben

Details zur Stelle

vor 8 Tagen

Qualifikationen

TensorFlow
NumPy
Master
Machine Learning
Keras
Python

Vollständige Stellenbeschreibung

Intern in the CAVES & PANGAEA Team, Machine Learning Development

Job Requisition ID: 20740

Date Posted: 1 July 2026

Closing Date: 29 July 2026 23:59 CET/CEST

Publication: External Only

Type of Appointment: Intern

Directorate: Human and Robotic Exploration

Workplace: Porz-Wahn, DE

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 on 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, consisting of 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 together with data fusion from multi-method spectroscopy, allowing a drastic increase in the accuracy of automatic mineral and rock recognition.

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

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

You are sought to 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.

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.

You should possess practical experience with Machine Learning-based classification and regression methods, including Convolutional Neural Network (CNNs) and/or Kolmogorov-Arnold Networks (KANs).
Strongly desirable: familiarity with advanced ML methodologies such as multi-modal data fusion, signal decomposition/unmixing, incremental/few-shot learning, and conformal prediction. Academic or professional proficiency in Python and its core scientific/ML frameworks: TensorFlow, JAX, Keras, and Scikit-learn and NumPy. Hands-on experience processing, analyzing, and visualizing scientific data obtained from analytical instrumentation, and working with datasets and databases are also valuable. Familiarity with MLOps software concepts (model/dataset integration, pipeline automation). A valuable asset: basic understanding of physical/chemical data metrics (e.g., spectral analysis, stoichiometric consistency) or interpretable and explainable (physics informed) ML concepts and tools.

Diversity, Equity and Inclusiveness
ESA is an equal opportunity employer, committed to achieving diversity within the workforce and creating an inclusive working environment. We therefore welcome applications from all qualified candidates irrespective of gender, sexual orientation, ethnicity, religious beliefs, age, disability or other characteristics.

At the Agency we value diversity, and we welcome people with disabilities. Whenever possible, we seek to accommodate individuals with disabilities by providing the necessary support at the workplace. The Human Resources Department can also provide assistance during the recruitment process. If you would like to discuss this further, please contact us via email at [email protected].

Important Information and Disclaimer
During the recruitment process, the Agency may request applicants to undergo selection tests.

Applicants must be eligible to access information, technology, and hardware which is subject to European or US export control and sanctions regulations.

The information published on ESA’s careers website regarding internship conditions is correct at the time of publication. It is not intended to be exhaustive and may not address all questions you would have.

Nationality

Please note that applications are only considered from nationals of one of the following States: Austria, Belgium, Cyprus, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, the Netherlands, Norway, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland, and the United Kingdom.

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