Job Description
This master thesis aims to develop a multi-modal retrieval-augmented generation (RAG) system capable of integrating textual, visual and structured knowledge to perform robust multi-hop question answering. Such a system has practical applications in areas like manufacturing, autonomous driving and multimedia search.
* In your master thesis you will develop a multi-modal RAG model that integrates combined multi-modal information. You will also benchmark existing baselines.
For the master thesis topic, one of the following research questions can be chosen:
* How can multi-modal data (text, images, knowledge graphs) be effectively integrated within a retrieval-augmented generation framework for multi-hop question answering?
* How robust is the multi-modal RAG model under realistic domain shifts or noisy/missing modalities?
Qualifications
* Education: Master studies in the field of Computer Science, Mathematics or comparable
* Experience and Knowledge: in large language models (LLMs), vision-language models (VLMs) and knowledge graphs; experience or willingness to learn deep learning frameworks (PyTorch); prior knowledge of multi-modal learning and retrieval models is preferred
* Personality and Working Practice: you are a motivated team player with strong communication skills
* Enthusiasm: passion for research and independent problem solving
* Languages: very good 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?
Hongkuan Zhou (Functional Department)
+49 174 1951055
Lavdim Halilaj (Functional Department)
+49 711 811 15838
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