Master Thesis in Multimodal Foundation Models with Knowledge Graphs for Visual Entity Recognition
Master Thesis in Multimodal Foundation Models with Knowledge Graphs for Visual Entity Recognition
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Job Description
- During your Master thesis you will learn stateoftheart 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 GNNbased 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 selfmotivation 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 4
Lavdim Halilaj (Functional Department)
49 8
#LIDNI
Remote Work :
No
Employment Type :
Fulltime
Employment Type
Full-time
