Master Thesis Graph Foundation Models for Enterprise Knowledge and Reasonin at Bosch Group

Renningen, Baden-Württemberg, Germany -

Full Time

Start Date

Immediate

Expiry Date

11 Aug, 26

Salary

0.0

Posted On

13 May, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Natural Language Processing, Foundation Models, Transformer Architectures, PyTorch, TensorFlow, Graph Neural Networks, Knowledge Graphs, Graph Foundation Models, Deep Learning, Link Prediction, Graph Analytics

Industry

Software Development

Description

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 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. Need further information about the job? Lavdim Halilaj (Functional Department) +49 711 811 10832 Mirjam Steger (Functional Department) +49 711 811 10832 Work #LikeABosch starts here: Apply now! #LI-DNI Legal Entity: Robert Bosch GmbH

Responsibilities

The candidate will conduct a literature review on Graph Foundation Models and develop innovative models to automate the construction and reasoning of enterprise knowledge graphs. Responsibilities include extracting structured entities from documents and evaluating model performance on academic and real-world Bosch datasets.