Master Thesis in Learning to Access the Wi-Fi Medium with Reinforcement Learning

At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people’s lives. Our promise to our associates is rock-solid: we grow together, we enjoy our work, and we inspire each other. Join in and feel the difference.

The Robert Bosch GmbH is looking forward to your application!

• During your Master Thesis, you will have a real impact on a future product developed by Bosch.
• You will improve the Wi-Fi Medium Access Control (MAC) sublayer's channel access using Reinforcement Learning (RL) in a novel way.
• Last but not least, you will extend the ns-3 network simulator to test and evaluate your ideas.

• Education: Master studies in the field of Computer Science, Communications Engineering, Electrical Engineering or comparable
• Experience and Knowledge: with Machine Learning, preferably in multi-agent or single-agent reinforcement learning; familiarity with ML frameworks, preferably PyTorch; experience with simulations, ideally using network simulators like ns-3; knowledge of wireless communication systems, preferably Wi-Fi
• Personality and Working Practice: you are an open-minded individual with an independent working style
• Languages: good in German or English

Start: according to prior agreement

Duration: 6 months

Requirement for this thesis is the enrollment at university. Please attach your CV, transcript of records, examination regulations and if indicated a valid work and residence permit.

You are almost finished with your Bachelor's degree and would like to gain some practical experience before embarking on your next academic adventure with a Master's degree? Then you fit in perfectly well with our PreMaster Programm! Take a look at our vacancies here.

Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore, we welcome all applications, regardless of gender, age, disability, religion, ethnic origin or sexual identity.

Need further information about the job?

Sebastian Lindner (Functional Department)

+49 5121 49 2907

Naomi Gomez-Stricker (Functional Department)

+49 5121 49 6809

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