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Job Description

Large Language Models (LLMs) have revolutionized natural language processing, yet they struggle to interpret and reason over the highly structured, interconnected data that powers modern enterprises. Knowledge Graphs (KGs) are a powerful mechanism for representing this structured knowledge, but building and utilizing them at scale remains a challenge. A new frontier is emerging with Graph Foundation Models (GFMs), which promise to bridge the gap between the generative power of LLMs and the structured reasoning of KGs. The vision of this thesis is to leverage the application of GFMs within the Bosch ecosystem. We aim to explore how these cutting-edge models can automate the construction, completion, and reasoning over our enterprise knowledge graphs.

• During your thesis you will conduct a comprehensive literature review of the state-of-the-art in Graph Foundation Models and their application. You will analyze existing benchmarks and datasets for knowledge graph construction, link prediction, and advanced graph-based analytics to identify key methodologies.
• Furthermore, you will develop innovative models and experiment with their implementation. You will use GFMs to extract structured entities and their relationships from internal Bosch documents and fine-tune or prompt GFMs to infer and predict missing links and relationships within our existing knowledge graphs.
• You will develop methods to translate natural language questions into formal graph queries or use the GFM to reason over graph pathways, directly supporting use cases like root-cause analysis in manufacturing.
• Finally, you will rigorously evaluate the performance of the developed models on both standard academic benchmarks and on real-world Bosch datasets and use cases. You will analyze the scalability, robustness, and deployment potential of the developed methods within Bosch's enterprise environment.

Qualifications

• Education: Master studies in the field of Computer Science or comparable
• Experience and Knowledge: strong academic background in machine learning and natural language processing; solid understanding of foundation models and transformer architectures; hands-on experience with deep learning frameworks (e.g., PyTorch, TensorFlow); familiarity with graph data structures, graph neural networks, and related concepts is a plus
• Qualification: Bachelor’s degree in Computer Science
• Personality and Working Practice: you are a motivated and research-oriented student with a proactive and independent approach to problem-solving
• Work Routine: your on-site presence is required
• Enthusiasm: keen interest in problem-solving
• Languages: 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. #LI-DNI