Data Processing for Utility Infrastructure
In the UtilityTwin research project, an intelligent digital twin for any energy or water supply network is to be researched and developed on the basis of adaptive high-resolution sensor data (down to the sub-second range) and machine learning techniques. Overall, the technology concepts BigData and AI are to be combined in an innovative way in this research project in order to make positive contributions to the implementation of the energy transition and to counteract climate change.
- Understand the state-of-the-art methods and technology for knowledge graph, identify various application fields in the industry
- Support the development of our framework to establish a digital twin for utility infrastructure written in Python
- Support the maintenance of graphical database for utility data deployed using Neo4j on MS AZURE environment
- Support the enrichment and expansion of the knowledge graph by utilizing ML techniques
- You are currently enrolled as a student at Friedrich-Alexander University and studying computer science, mathematics, physics, or a related field
- Hands-on programming experience with Python and familiar with machine learning techniques
- Preferably prior experience with various database techniques, such as SQL or graphical database
- Curiosity and pro-active mind
- An interesting application-oriented new field of research with contribution for sustainable utilization of nature resources
- Extensive scientific support
- Flexible way of working
- Friendly and open environment at the Pattern Recognition Lab.