Intern in Federated Learning for collaborative Air Combat (d/f/m)

Job Description:

In order to support Mission Systems, Airbus Defence and Space is looking for an

A vacancy for an intern (d/f/m) in Federated Learning for collaborative air combat (d/m/f) has arisen within Airbus Defence and Space in Manching. The successful applicant will join the Mission Systems department.

Federated learning is a promising approach to train collaboratively machine learning models. This technique has a lot of potential applications, especially for collaborative air combat involving several flying platforms (aircraft and UAV).

We are looking for a creative intern who is willing to work on supervised and unsupervised learning methods for sensor data understanding in distributed and resource-constrained environments.The successful applicant will contribute to the development and evaluation of novel ML techniques applied to sensor data in the context of real-world systems.

• Location: Manching

• Start: as soon as possible

• Duration: 3-6 months

Located about an hour’s drive north of Munich, Manching is an up-and-coming market town that offers a wide range of leisure and cultural activities. Here, you can enjoy the quality of life in the countryside while the pleasures of near-by cities are still within easy reach.

• A final thesis is possible after consultation with the department.

• International environment with the opportunity to network globally.

• Work with diversified technologies.

• At Airbus, we see you as a valuable team member and you are not hired to brew coffee, instead you are in close contact with the interfaces and are part of our weekly team meetings.

• Opportunity to participate in the Generation Airbus Community to expand your own network.

• Research and implement state-of-the-art clustering and classification algorithms.

• Design and implementation of evaluation pipelines.

• Experimentation on standalone devices or distributed setups (Raspberry Pi cluster, Kubernetes).

• Visualization and interpretation of results.

• Documentation and presentation of findings.

• Enrolled fulltime student (d/f/m) within Data Science, Computer Vision, Deep Learning, Machine Learning or related field of study.

• Interest in algorithmic machine learning, evaluation metrics and experimental research.

• Project experience in Data Science.

• Familiar with scientific research domain.

• Programming experience with Python, including PyTorch, TensorFlow, or scikit-learn.

• Experience with Version Control Systems e.g., GIT.

• Experience with clustering/classification algorithms is a plus.

• Experience with Docker and Kubernetes is a plus.

• Good communication skills in English.

Please upload the following documents: cover letter, CV, relevant transcripts, enrollment certificate. 

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