41 algorithm-development-"Prof"-"Prof"-"Prof" PhD positions at Cranfield University in United Kingdom
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, such as imbalance and misalignment, facilitating the development and validation of diagnostic and prognostic algorithms. Electronic Prognostics Systems: Facilities equipped to assess the health and predict
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This research opportunity invites self-funded PhD candidates to develop advanced deblurring techniques for retinal images using deep learning and variational methods. Retinal images often suffer
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vulnerabilities like side-channel attacks and unauthorized access, which can compromise system integrity. Developing robust security measures within AI-enabled electronics is essential for applications in defence
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and misalignment, facilitating the development and validation of diagnostic and prognostic algorithms. Electronic Prognostics Systems: Facilities equipped to assess the health and predict the remaining
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prototype/demonstrator of a low-cost smart sensor. To develop an efficient algorithm to process the vibration signals locally and to develop the firmware to be embedded within the sensor node. To validate
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-disciplinary approach that integrates design, technology and management expertise. We link fundamental materials research with manufacturing to develop novel technologies and improve the science base
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) of high-value critical assets. Through this PhD research, algorithms and tools will be further improved and developed, validated and tested. It is expected that combining the domain knowledge and the
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trust in digital communications and readily bypass conventional security controls. This PhD research proposes to design, develop, and validate a novel, explainable, multi-modal detection framework. By
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, government, and wider society. In the REF2021 review of UK university research, 88% of Cranfield’s research was rated as ‘world-leading’ or ‘internationally excellent’. This project will develop a robust
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this issue and we could use obtain data-driven models using machine learning algorithms such as artificial neural networks, reinforcement learning, and deep learning. A typical caveat of data-driven modelling