These are your tasks:
- Analyze smart meter and sensor data from simulated and real-world water distribution networks to identify patterns related to leaks.
- Develop and implement machine learning models for leak detection and localization in water distribution systems.
- Evaluate and compare different machine learning approaches for anomaly detection and assess their suitability for practical applications.
- Validate model performance under varying network conditions and investigate the robustness and limitations of the developed methods.
- Document your findings, derive recommendations for real-world deployment, and collaborate closely with experts from Diehl Metering and academic research partners.
Who we are looking for:
- You are enrolled in a Master's degree program in Computational Engineering, Computer Science, Data Science, Physics, Mathematics, or a related engineering or scientific discipline.
- You have solid programming skills in Python and a strong interest in data-driven problem solving.
- You bring basic knowledge of Machine Learning and ideally have experience working with time-series data and data analysis techniques.
- You are characterized by a structured and independent working style, strong analytical thinking, and enthusiasm for tackling research-oriented challenges.
- You are a proactive team player with excellent communication and presentation skills, very good English proficiency, and a strong motivation to learn and apply new Machine Learning technologies and methods.
Note: Applicants (m/f/d) with a severe disability will be given preference if equally qualified. If you wish, please indicate your severe disability status in your application—on a voluntary basis.
Diese Benefits bieten wir dir - und mehr:
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Flexible Arbeitszeiten
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Familienunternehmen
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Homeoffice
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Kantine
Mach bei Diehl, was dir wichtig ist.
Diehl Metering GmbH
Elisa Sophie Bergmann
Industriestraße 13, 91522 Ansbach
Tel: +49 981 1806-536
