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Master Thesis Graph Foundation Models for Enterprise Knowledge and Reasoning(m/w/x)
Developing graph foundation models for enterprise knowledge and reasoning using internal Bosch documents. Strong academic background in ML and NLP required. Hands-on experience with deep learning frameworks and familiarity with GNNs a plus.
Anforderungen
- Master studies in Computer Science or comparable
- Strong academic background in machine learning and NLP
- Solid understanding of foundation models and transformer architectures
- Hands-on experience with deep learning frameworks
- Familiarity with graph data structures, GNNs, and related concepts is a plus
- Bachelor’s degree in Computer Science
- Motivated and research-oriented student
- Proactive and independent approach to problem-solving
- Keen interest in problem-solving
Aufgaben
- Conduct comprehensive literature review on Graph Foundation Models
- Analyze benchmarks and datasets for knowledge graph construction
- Identify key methodologies for link prediction and graph analytics
- Develop innovative models for knowledge graph construction
- Experiment with GFM implementation on internal Bosch documents
- Extract structured entities and relationships from documents
- Fine-tune or prompt GFMs to infer missing links and relationships
- Translate natural language questions into formal graph queries
- Reason over graph pathways for use cases like root-cause analysis
- Evaluate model performance on academic benchmarks and Bosch datasets
- Analyze scalability, robustness, and deployment potential of methods
Ausbildung
- Bachelor-AbschlussODER
- Master-Abschluss
Sprachen
- Englisch – fließend
Tools & Technologien
- PyTorch
- TensorFlow
- graph neural networks
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Master Thesis Graph Foundation Models for Enterprise Knowledge and Reasoning(m/w/x)
Developing graph foundation models for enterprise knowledge and reasoning using internal Bosch documents. Strong academic background in ML and NLP required. Hands-on experience with deep learning frameworks and familiarity with GNNs a plus.
Anforderungen
- Master studies in Computer Science or comparable
- Strong academic background in machine learning and NLP
- Solid understanding of foundation models and transformer architectures
- Hands-on experience with deep learning frameworks
- Familiarity with graph data structures, GNNs, and related concepts is a plus
- Bachelor’s degree in Computer Science
- Motivated and research-oriented student
- Proactive and independent approach to problem-solving
- Keen interest in problem-solving
Aufgaben
- Conduct comprehensive literature review on Graph Foundation Models
- Analyze benchmarks and datasets for knowledge graph construction
- Identify key methodologies for link prediction and graph analytics
- Develop innovative models for knowledge graph construction
- Experiment with GFM implementation on internal Bosch documents
- Extract structured entities and relationships from documents
- Fine-tune or prompt GFMs to infer missing links and relationships
- Translate natural language questions into formal graph queries
- Reason over graph pathways for use cases like root-cause analysis
- Evaluate model performance on academic benchmarks and Bosch datasets
- Analyze scalability, robustness, and deployment potential of methods
Ausbildung
- Bachelor-AbschlussODER
- Master-Abschluss
Sprachen
- Englisch – fließend
Tools & Technologien
- PyTorch
- TensorFlow
- graph neural networks
Gefällt dir diese Stelle?
BetaDein Career Agent findet täglich ähnliche Jobs für dich.
Über das Unternehmen
Robert Bosch Stiftung GmbH
Branche
IT
Beschreibung
Das Unternehmen gestaltet Zukunft mit hochwertigen Technologien und Dienstleistungen, die das Leben der Menschen verbessern.
Noch nicht perfekt?
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VollzeitPraktikumnur vor OrtRenningen - Bosch Group
Master Thesis Data-Efficient Hybrid Machine Learning for Robust Vibration System Prediction(m/w/x)
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PhD Thesis - Scalable and explainable End2End Driving Model (SEED)(m/w/x)
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