Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Working hours

Regular working hours

Languages

English

Job source

www.indeed.com

Tech stack

Data Analysis Python (Programming Language) Machine Learning Information Technology Data Analytics

Job description

• 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.

Requirements

• 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.

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