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We are looking for 4 highly motivated PhD students to join our research team for a project focused on battery modelling, state estimation, fault diagnosis and control. Information Batteries
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to join our research team for a project focused on battery modelling, state estimation, fault diagnosis and control. Information Batteries are at the core of the sustainable energy transition by powering
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focuses on AI-driven fault diagnosis, predictive analytics, and embedded self-healing mechanisms, with applications in aerospace, robotics, smart energy, and industrial automation. Based
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This PhD project will focus on developing, evaluating, and demonstrating an intelligent solution of diagnosis and prognosis for rotating machinery to enhance safety, reliability, maintainability and
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Congenital Hyperinsulinism (CHI) is a rare inborn error of metabolism where the pancreas produces excessive insulin, resulting in persistently low blood sugar, leading to seizures and brain damage
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Aerial Vehicles (UAV), e.g. drones, are increasingly used for equipment anomaly and fault detection in offshore wind turbines. When the drones are employed to take images, the quality of the images can be
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diagnosis methodologies that meet these challenges head-on. You will dive into areas such as: AMS fault modeling. AMS test stimuli and detection generation. Automatic AMS test pattern generation and
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on the following: Combining passive and active cooling strategies. To optimize sensor types, the number of sensors, and locations withing cooling system and building to facilitate efficient monitoring and fault
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of the challenges is fault detection and diagnosis of bearings subject to low (rotational) speed. As vibration/acoustic signals generated by the faults of low-speed bearings are very weak and often covered by strong
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interpretation, trust, error tracing etc. XAI methods offer clear-box models, often comparable to DL in accuracy, yet better in 1st-time scenarios (where the AI has not exactly encountered the given situation with