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-making processes. The objective of this PhD project is to develop general methods for Fault Detection, Isolation, and Recovery (FDIR) in systems governed by hyperbolic partial differential equations
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detect anomalies and faults occurring during operation. As such, it will involve the development of state-of-the-art deep-learning techniques for computationally efficient models, and validation
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hardware design, and build in fault detection and correction to ensure secure, efficient operation in space systems. The outcome will be a high-performance, fault-tolerant Falcon implementation, enhancing
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of Twente is looking for a highly motivated and talented PhD candidate to join our team. In this position, you will explore advanced frameworks to make complex interacting programs fault-tolerant and future
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. Importantly, there are two alternative theories on how error signals in predictive processing could be coded in neural signals: either as (1) top down signals from ‘higher order’ brain areas (hierarchical
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Science, Robotics, or similar. A background in digital twin engineering, autonomous systems, and machine learning is required. An understanding of machine learning and MLOps is desired. Fault detection, multi
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Autonomous methods for fault or anomaly detection and classification of PV plants with high accuracy are necessary for the monitoring of large-size PV power plants. Objectives also include other
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explainable” machine-aided decision support for Safety and Mission Critical objectives e.g. fault detection/tracing, evasive manoeuvring, target selection etc. Detailed semantic understanding of operational
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-Time Structural Health Monitoring (SHM): Sensor-integrated ML models will be developed to analyze real-time data from installed wind turbine towers, enabling early fault detection and predictive
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design monitoring devices that seamlessly integrate these sensors with existing logging and fault-detection systems in electronic devices. Additionally, s/he will explore database-processing techniques