Job Description
Do you want beneficial technologies being shaped by your ideas? Whether in the areas of mobility solutions, consumer goods, industrial technology or energy and building technology - with us, you will have the chance to improve quality of life all across the globe. Welcome to Bosch.
The Robert Bosch GmbH is looking forward to your application!
Employment type: Limited
Working hours: Full-Time
Joblocation: Renningen
Knowledge Graphs (KGs) have proven to be a key factor to foster the success of Smart Manufacturing. The data generated in these contexts need to be semantically harmonized and made available for a myriad of applications.
However, due to the huge level of heterogeneity, as well as the underlying data quality, there exist the requirement to continuously update and improve these KGs by means of knowledge graph completion (KGC) methods like Link Prediction or Node Classification. Besides, state-of-the-art KGC approaches fail to consider the quality constraints while generating predictions, resulting in the completion of KGs with erroneous relationships.
- To successfully adopt KGs and its enhancements made by KGC methods, a Neuro-Symbolic approach is required. The approach will enable manufacturing experts to understand and apply the output obtained by the methods and properly evaluate whether the results make sense according to their knowledge.
- This thesis will research innovative Neuro-Symbolic methods to improve KGC methods in smart manufacturing scenarios. Emphasis will be placed on the representation of heterogenous and relation-based knowledge and the combination with learning-based approaches to enable explainability and transparency of the predictions.
- The thesis will validate the methods on one or more real use cases at Bosch. Evaluations will be performed on public benchmarking challenges in smart manufacturing recognized by the community and internal Bosch datasets.
- Education: excellent master's degree in the field of Computer Science, Mathematics, Engineering, or a related field (please provide your degree marks)
- Experience and Knowledge: strong experience in machine learning and preferable in graph embedding and graph neural networks, knowledge in deep learning frameworks (PyTorch); good programming skills in Python and presentation skills as well as basic knowledge of semantic methods and ontologies
- Personality and Working Practice: you can approach others openly, communicate your ideas clearly and actively seek out new challenges
- Languages: fluent in English, German is a plus
- Work-life balance: Flexible working in terms of time, place and working model.
- Health & Sport: Wide range of health and sports activities.
- Childcare: Intermediary service for childcare services.
- Employee discounts: Discounts for employees.
- Room for creativity: Space for creative work.
- In-house social counseling and care services: Social counselling and intermediary service for care services.
The recruitment contact or superior will be happy to provide information about the individual benefit plan.
View More