Thesis in Neuro-Symbolic Methods for Analysis in Manufacturing

Vor 4 Tagen

Renningen, Deutschland bosch Vollzeit

Job DescriptionJoin our team and work in a rapidly evolving field of AI research, bringing together symbolic and sub-symbolic worlds.Your research will involve incorporating symbolic manufacturing knowledge into neural analysis, such as deep graph networks for decision support.Furthermore, you will contribute and exchange with leading scientists at our Bosch Center for Artificial Intelligence (BCAI).You will have the opportunity to lay the foundation for your research career by publishing as a co-author at a top-tier scientific conference or journal.Additionally, you will solve real-world scenarios in manufacturing and help ensure the highest quality of Bosch products.Last but not least, your contribution will improve efficiency and reduce waste on the production lines.QualificationsEducation: studies in the field of Computer Science, Physics, Engineering or comparableExperience and Knowledge: sound knowledge of AI and interest in working on neuro-symbolic methods, knowledge modelling or deep graph networks such as graph neural networks or graph convolutional networksPersonality and Working Practice: open-minded, cooperative, decisive and collaborativeEnthusiasm: passion for scientific work and willingness to publish results, as well as interest in in-depth technical discussions on the topicLanguages: fluent in EnglishAdditional InformationStart: according to prior agreementDuration: 6 monthsRequirement 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? Tim Strobel (Functional Department) #LI-DNI

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