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This PhD at Cranfield University explores the development of resilient, AI-enabled electronic systems capable of detecting faults and autonomously recovering from failures in real time. The project
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-driven shifts in species distributions. Currently, barnacles and other species are manually counted from over 3,000 images each year, which is time-consuming and prone to human error. This project will
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Reference Number BI|2025/785 Project Livranças Digitais2 (I3PR004035) Is the Job related to staff position within a Research Infrastructure? No Offer Description Public notice for one research grant Refª BI
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of Robot Behaviours in Simulation: Develop methods to automatically generate diverse test scenarios in a virtual environment to efficiently find faults in robotic skills. This involves using intelligent
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downtime and operational costs. Traditional condition monitoring approaches often face challenges in accurately detecting early-stage faults, especially in the presence of highly impulsive signals
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Fuel Rig with Five Degradation Faults: Simulates various degradation scenarios in unmanned aerial vehicle (UAV) fuel systems, enabling research into fault detection, isolation, and prognostics. Machine
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control system that enhances Annual Energy Production (AEP), reduces mechanical stress, and improves fault detection using machine learning (ML) and physics-based modelling. The candidate will gain hands
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fault detection. Information Self-awareness in humans is an innate capability, arising from the brain’s ability to process a multitude of sensory inputs. Emulating this functionality in electronic systems
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suite of specialised facilities: UAV Fuel Rig with Five Degradation Faults: Simulates various degradation scenarios in unmanned aerial vehicle (UAV) fuel systems, enabling research into fault detection
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research into fault detection, isolation, and prognostics. Machine Fault Simulator for Rotating Machinery Faults: A versatile platform that replicates common faults in rotating machinery, such as imbalance