Masterthesis: Datenbasierte Kausalitätsanalysen und Predictive Maintenance in der Wärmeversorgung
Core
Develop data-driven methods for causal analysis of disturbances in energy plants and heat networks to automate root cause reconstruction and enable predictive maintenance.
Role type
Master's thesis researcher (data science & causal inference)
Builds
Automated disturbance analysis and predictive maintenance solutions for energy supply
Domain
Energy sector / Heat supply / Data Science
Deliverable
research
Required skills
Causal inference, Time series analysis, Anomaly detection, Python (Pandas, scikit-learn, TensorFlow, PyTorch), Big Data handling, Machine learning
Preferred skills
Physics modeling, Applied mathematics
Responsibilities
Research and analyze causal analysis and anomaly detection procedures, Apply methods to real measurement data from heat networks, Adapt and extend existing methods, Critically evaluate results for automated analysis potential
