Masterarbeit KI-basierte Anomalieerkennung in der robotergestützen Fertigung
Core
Developing a machine learning methodology for anomaly detection in robot-assisted manufacturing systems by analyzing heterogeneous industrial time-series data.
Role type
Master's thesis researcher (AI/ML)
Builds
Production-ready anomaly detection models for industrial robotics use cases
Domain
Industrial manufacturing, robotics, predictive maintenance
Deliverable
production ML models
Required skills
Machine Learning, Deep Learning, Time-Series Prediction, Unsupervised Learning, Python, Data Pipeline Development, Exploratory Data Analysis
Preferred skills
Power BI, Tableau, Heterogeneous time-series data handling
Technologies
Python, Machine Learning frameworks, Deep Learning frameworks
Responsibilities
Research state-of-the-art methods for unsupervised learning and anomaly detection; develop and evaluate AI models on real industrial robotics data; build data pipelines for preprocessing and normalization; collaborate with cross-functional teams; document and analyze results scientifically.
Seniority
Master's student level
