Masterarbeit: Automatisierte Sensorkalibrierung und Fehlerklassifizierung für intelligente Sensornetzwerke (w/m/div.)
Core
Developing an automated system to classify sensor failures (hardware anomalies vs. software drifts) and trigger software-based recalibration for industrial machinery reliability.
Role type
Master's thesis researcher (Machine Learning & Sensor Systems)
Builds
Self-calibrating sensor systems for industrial automation
Domain
Industrial automation, sensor technology, machine learning
Deliverable
production ML models
Required skills
Python programming, Data Science libraries (Pandas, NumPy, Scikit-learn), Machine Learning classification, Physical measurement principles, Sensor technology, Git/GitHub
Preferred skills
Collaborative software development, Analytical thinking
Technologies
Python, Pandas, NumPy, Scikit-learn, Git, GitHub
Responsibilities
Conduct physical measurements on a ball screw drive test bench to capture sensor data and failures; Analyze datasets to distinguish software-correctable drifts from hardware errors; Design an automated workflow for software-based recalibration; Validate concepts with hardware and drift datasets; Propose strategies for handling cases where software recalibration is insufficient.
Seniority
Master's student (Research Project)