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# Working student - Machine Learning

- **Company:** Deutsches Zentrum für Luft- und Raumfahrt e. V.
- **Location:** Weßling, Germany
- **Salary:** €6,240.0 - €10,400.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Computer Programming, Continuous Integration, Python (Programming Language), Machine Learning, Supervised Learning, Deep Learning, Git, Information Technology, Machine Learning Operations, Software Version Control, Programming Languages
- **Published:** August 18, 2026
- **Apply:** https://www.adzuna.de/details/5829109626

## About the Role

* Enrolment in a scientific Master's degree programme, preferably in computer science, mathematics, statistics, data science, aerospace or a comparable scientific and technical degree programme
 * Good knowledge of at least one programming language, preferably Python
 * Experience in dealing with version control and modern development processes, ideally with Git and CI/CD
 * Good written and spoken English skills
 * Solid knowledge of statistics as well as machine learning and deep learning
 * Ideally initial experience with advanced topics such as federated learning, explainable AI, anomaly detection in time series or self-supervised learning
 * Ideally knowledge or practical experience in analysing time series data
 * Ideally initial experience in scientific work, for example through seminar papers, project work or theses as well as in writing scientific texts
 * Ideally submit code examples, a Git repository or other evidence of practical programming experience with your application

## Description

You will work on the implementation of applicable machine learning pipelines for existing simulation environments or hardware-related systems as well as on the scientific investigation of sophisticated ML methods and architectures. A particular focus is on unsupervised and semi-supervised learning methods, especially for the analysis, modelling and evaluation of time series data.

Your tasks

 * Evaluation and validation of selected machine and deep learning methods using available data sets and benchmarks
 * Design, implementation and further development of applicable ML pipelines for time series data
 * Integration and deployment of developed solutions in existing simulation environments and/or hardware-related systems
 * Scientific analysis and evaluation of advanced ML concepts, methods and architectures with regard to their applicability in the space domain
 * Independent familiarisation with new scientific issues and development of relevant literature and methods
 * Preparation, documentation and presentation of results
 * Collaboration on scientific publications


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