Master Thesis in Continual Pre-Training of Neural Networks

Company Description

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!

Job Description

Are you ready to dive into the exciting world of artificial intelligence and machine learning? Join us on a thrilling journey as we explore the cutting-edge field of Continual Learning! 

• During your thesis you will conduct research on Continual Learning and on (self-supervised) pre-training methods. Discover how these methods can enhance the performance of deep neural networks and contribute to the development of smarter systems.
• You will get hands-on experience by implementing and adapting state-of-the-art research methods using PyTorch.
• Furthermore, you will put your skills to the test as you train deep neural networks on various data types with Python/Pytorch.
• Finally, you will learn how to analyze and visualize datasets using Python, transforming raw data into compelling stories that highlight your findings and showcase your results.

Qualifications

• Education: Master studies with a completed bachelor's degree in the field of Computer Science, (Applied) Mathematics or comparable
• Experience and Knowledge: in-depth experience in Python programming, with PyTorch, Git and machine learning; experience with software engineering and code versioning is an advantage
• Personality and Working Practice: you demonstrate motivation by proactively taking on tasks, developing solutions independently, and approaching problems critically and analytically to make informed decisions
• Work Routine: for onboarding, a one-week presence in Renningen is required; after that, a minimum of one visit per month for three consecutive days is desired
• Enthusiasm: for machine learning
• Languages: fluent in English and very good in German

Additional Information

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.

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?
Tobias Schlagenhauf (Functional Department)
+49 711 811 16677
Pascal Janetzky (Functional Department)
+49 173 4163104

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