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

The goal of this master thesis is to explore innovative methodologies for enhancing the efficiency of deep neural networks in artificial intelligence applications, particularly in robotics and autonomous driving.

During your thesis you will work on developing a temporal predictive extension of existing foundation models to identify and eliminate redundancy in temporal high-dimensional data, such as video streams. This approach aims to significantly reduce runtime inefficiencies associated with sequential computations, thereby streamlining the inference process and improving overall model performance.You will conduct a comprehensive literature review to understand current methodologies.Furthermore, you will design a novel model extension that incorporates temporal predictive capabilities, and you will implement training protocols to optimize the model's performance on temporal data.Finally, you will evaluate the model's efficiency in terms of computational and energy consumption and analyze the implications of the findings for real-time AI applications.

QualificationsEducation: Master studies in the field of Natural Sciences and Engineering (like Computer Science, Math, Statistics, Physics, Cybernetics, Electrical Engineering) with very good gradesExperience and Knowledge: strong knowledge and experience in Machine Learning, Deep Learning, Computer Vision, and Python (PyTorch)Personality and Working Practice: you excel at working independently and driving your own projects forward with strong intrinsic motivationLanguages: fluent in English

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?
Volker Fischer (Functional Department)
+49 1525 8813453

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