Internship/thesis Within Deep Reinforcement

vor 3 Monaten

Immenstaad am Bodensee, Deutschland Airbus Defence and Space GmbH Vollzeit

In order to support the department Operational Analysis and Studies, Airbus Defence and Space is offering an

**Internship/Thesis within Deep Reinforcement Learning in combat simulations (d/f/m)**:
The department "Operational Analysis and Studies" at Airbus Defence and Space GmbH in Friedrichshafen is heavily involved in artificial intelligence related activities within Airbus. As study driven and therefore also research oriented department we are looking forward to explore the possibilities of artificial intelligence in the defense domain.

Your mission will be to support and setup innovative machine learning environments to implement and use state of the art reinforcement learning algorithms.
- **Location**:Friedrichshafen**:

- **Start**:As soon as possible**:

- **Duration**:4 - 6 month**:
**Your location**:
At the Airbus site in Friedrichshafen you will be working on innovation where others spend their holidays. Enjoy panoramic views of Lake Constance while having lunch in our canteen. And after work, join one of our many corporate sports groups to go running, sailing or skiing.

Your tasks and responsibilities

**Your benefits**:

- Attractive salary and work-life balance with a 35-hour week (flexitime).
- International environment with the opportunity to network globally.
- Work with modern/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.

**Your internship will cope with the following topics**:

- Setup Machine Learning environment based on Google Tensorflow and PyTorch with Python on a local cluster architecture using Rlib/Ray or on public cloud such as Google Cloud Platform (GCP) or Amazon AWS
- Design, train and evaluate different neural network architectures and algorithms for Deep Reinforcement Learning (e.g. PPO2, DQN)
- Support in the development and improvement of the simulation environment
- Development of state of the art reinforcement learning (RL) environment’s
- Evaluation of RL algorithms
- Setup of appropriate training architecture (e.g. server/cluster, MPI etc.)
- Support existing RL topics and introduce new perspectives/innovative ideas

**Desired skills and qualifications**:

- Enrolled student (d/f/m) within Information Technology, Industrial Engineering, Computing, Software or similar field of study
- Software skills (programming languages) and technical skills
- We are looking for an extremely self-motivated person, who is proactively contributing to all relevant topics and is keen in evaluating and improving different neural network architectures and designs
- We are looking for someone who loves to be challenged and who wants to go the extra mile
- The duration of your internship should be at least 4 months, and we would prefer having someone for 6 months or to directly continue with a bachelor or master thesis
- You should have an IT / Engineering background bringing with you basic knowledge in Deep Learning / Deep Reinforcement Learning as well as practical experiences in Python and Tensorflow and/or PyTorch
- English: good knowledge, German is a plus

Please upload the following documents**:cover letter, curriculum vitae, relevant certificates, certificate of enrollment, and, in the case of a mandatory internship, the relevant excerpt from the study regulations.**

This job requires an awareness of any potential compliance risks and a commitment to act with integrity, as the foundation for the Company’s success, reputation and sustainable growth.

**Company**:
Airbus Defence and Space GmbH

**Employment Type**:
Internship**Experience Level**:
Student

**Job Family**:
Software Engineering

At Airbus, we support you to work, connect and collaborate more easily and flexibly. Wherever possible, we foster flexible working arrangements to stimulate innovative thinking.

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