Master Thesis in Multimodal Foundation Models with Knowledge Graphs for Visual Entity Recognition
Job Description
- During your Master thesis, you will learn state-of-the-art methods for visual entity recognition, combining information from KGs, images and text.
- You will train advanced knowledge graph embedding methods, such as transE, transH, and some other GNN-based methods.
- Furthermore, you improve the VLMs like CLIP, PaLI etc..
- Last but not least, you will develop your own methods for combining KG and foundation models.
Qualifications
- Education : Master studies in the field of Computer Science, Artificial Intelligence or comparable
- Experience and Knowledge of KGs and VLMs is an advantage
- Personality and Working Practice : a person with a high self-motivation and passion for research
- 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)
49 152 037 33544
Lavdim Halilaj (Functional Department)
49 711 811 15838
LI-DNI
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