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from sensors or other continuous data sources. Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages
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to improve asset management. However, a key challenge in implementing predictive maintenance is the presence of data uncertainty arising from sensor noise, missing data, fluctuating operating conditions, and
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, particularly Python Familiarity with Graph Neural Networks (GNNs). Experience with sensor calibration, fusion techniques, and real-time motion analysis preferred. Ability to work collaboratively in
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