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Technik- und Ingenieurwissenschaften

Kaja Balzereit

E-Mail

kaja.balzereit@iosb-ina.fraunhofer.de

Alter

26

Aktuelle Tätigkeit

Promovend*in


Institution

Fraunhofer IOSB-INA - Abteilung Maschinelle Intelligenz

Lemgo

Nordrhein-Westfalen


Biographie

Kaja Balzereit has been a research associate at the Fraunhofer Industrial Automation branch INA of Fraunhofer IOSB for more than three years now. For her PhD, she is researching symbolic AI methods for intelligent fault handling in production systems, in order to increase the resilience of modern production facilities to external, as well as internal, disturbances. She regularly publishes and presents the results of her research at international conferences and in scientific journals. In research projects, she works closely with various companies to bring the potential of AI into broad application and the future trend Industry 4.0 from theory into practice. Among other things, she recently contributed to an AI milestone on the road to the autonomous factory with the successful completion of a scientific project. In addition, her work is closely linked to the Fraunhofer-Gesellschaft's Machine Learning Research Center, which promotes the development of key technologies in Artificial Intelligence. Parallel to her scientific work, Kaja Balzereit is personally interested in inspiring students for her field of work. She has presented her work in several lectures at different universities and thus contributed to making AI research visible and inspiring students for technical topics and points out career paths for research in the STEM field.

Fragestellungen im Themenfeld Künstliche Intelligenz

Kaja Balzereit deals with intelligent error handling in modern production plants. In order to increase the resilience of production plants, she uses both symbolic AI methods and machine learning. The goal is not only to detect anomalies and errors in production plants, but also to automatically identify the cause of the errors as well as to minimize the effects of the errors as far as possible. Therefore, symbolic AI methods such as automatic reasoning which enable the analysis of cause-effect relationships and causalities are used.