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

Patent retrieval is a highly important task for patent professionals in many stages of the patent lifecycle. Current retrieval systems use term frequencies or neural embeddings to retrieve relevant prior art from a large index. LLMs could function as powerful rerankers and improve retrieval results.

Your Master thesis aims to assess the efficacy of LLM-based rerankers for patent retrieval.Furthermore, you will research state-of-the-art methods from the literature and analyze their performance in the context of patent data.In addition, you will implement computational experiments to identify advantages over prior methods, as well as areas for improvement and develop novel approaches to address identified limitations.If interested, you can participate in publishing the work at a major NLP conference or workshop.

QualificationsEducation: Master studies in the field of AI, Computer Science, Software Engineering or comparable with very good gradesExperience and Knowledge: profound knowledge of Machine Learning, Large Language Models, programming in Python and PyTorch; experience with Information Retrieval, Linux and LaTeX; prior knowledge of patents is an advantagePersonality and Working Practice: you excel at working autonomously, systematically organizing your tasks, and applying analytical thinking to solve complex problemsWork Routine: mobile working within GermanyLanguages: proficient in German or English

Additional Information

Start: according to prior agreement
Duration: 6 months

You want to work flexibly from your home in Germany or prefer working at the Bosch location in Renningen? For positions with the addition “remote possible”, you can agree on the appropriate collaboration for your task together with your manager and your team within the framework of Smart Work.

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?
Valentin Knappich (Functional Department)
+49 173 637 4092

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