(Senior) Researcher (f/m/d) - Foundation and World Models on Structured Business Data

(Senior) Researcher (f/m/d) - Foundation and World Models on Structured Business Data

At SAP, we keep it simple: you bring your best to us, and we’ll bring out the best in you. We touch over 20 industries and 80% of global commerce.

At SAP’s Business AI Research we explore the next frontier of AI methods on structured business data – graphs, tables, time series, and more.

Pioneer technical advances in foundation models on structured data and explore world models for solving business problems.

Gain new insights by integrating rich semantic context with large‑scale business data.

Enable academic research by providing business‑AI relevant problem statements, frameworks, and data sets.

Keep the pulse with novel research, providing insights into the latest findings and comparing them with internal work.

Translate applied research breakthroughs into research publications at top‑tier conferences and contribute to thought leadership in foundation and world models on structured data.

Collaborate with the team developing the relational pre‑trained transformer (SAP‑RPT‑1) to align internal initiatives and project deliverables with community best practices.

Collaborate with established academic partners and supervise PhD students during summer internships or academic collaborations.

• PhD in Computer Science, Artificial Intelligence, physics, mathematics or other relevant disciplines.
• Academic background in Foundation Models / Machine Learning on large‑scale structured data, or World Models, with a substantial record of related publications at top‑tier scientific conferences.
• Extensive research experience with machine learning on structured data and a deep understanding of how this translates to developing foundation models on structured data.
• Experience in Causal AI and its relation to foundation models on structured data.
• Proficiency in Python and strong hands‑on experience with ML frameworks such as PyTorch, TensorFlow, or similar.
• Optionally, hands‑on experience with knowledge graph technologies.
• Ideally, professional experience with the combination of knowledge graphs and large language models in an ERP domain.

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