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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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, impacting everything from cardiovascular to mental health. Despite this, women face significant delays in diagnosis and treatment, often experiencing a trial-and-error approach due to a lack of comprehensive
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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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and grippers offer improved safety and adaptability but introduce new challenges in design and control. Their development is still largely bio-inspired and trial-and-error based. Integrating flight and
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to ensure the modelling is correct. In our previous work and other projects, we routinely achieve agreement with the experimental data that is well within the experimental error. Within this PhD project, we
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of pollution (e.g. CO2 valorisation). However, current methods for discovering and optimising MOFs rely on trial-and-error, are poorly reproducible and scale-up takes many years/is not possible as conditions
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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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stressors, including public scrutiny, and abuse both on and off the field. Failure to effectively cope with these demands can lead to increased error, inability to deal with non-sport life events, which may
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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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metabolism, inborn errors of fatty acid metabolism, mitochondrial metabolism, and lipidomic for an associate professor position in the non-tenure stream. Applicants must have an MD or MD-PhD degree