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algorithms. This is an exciting multidisciplinary PhD project that promises to make cutting-edge advances in all research areas involved. Aim This project combines practical hands-on optics experimentation
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of diagnostic and prognostic algorithms. Electronic Prognostics Systems: Facilities equipped to assess the health and predict the remaining useful life of electronic components, supporting studies in electronic
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within the climate change domain. The techniques are based on statistical and computational approaches, including machine learning algorithms. The project aims first to contribute to the prevention of fake
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analytics, anomaly detection, and embedded redundancy to enhance system resilience. Students will focus on creating adaptive algorithms and hardware implementations that enable real-time diagnostics and
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propagation models that incorporate the effects of fire effluents, validated through controlled experimentation. You will develop tomographic inversion methods and anomaly-detection algorithms capable
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-of-the-art deep learning algorithms (e.g., CNNs, RNNs) to identify characteristic signatures of early airway disease. This project is designed for real-world impact. Through established clinical and industry
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multi-disciplinary, conducting innovative, internationally recognised research in power electronics systems. We specialise in the design and implementation of hardware and control algorithms for real
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-terminal antennas and beamforming operating in FR1 bands and future FR-2, enabling robust terrestrial–satellite integration for safety-critical air mobility services. To develop AI-based algorithms
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industries. Challenges include algorithmic bias, data privacy, and the erosion of trust in digital environments. Research questions include: How can design and creative methodologies foster transparency and
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reliable decision-making. Evolutionary algorithms will be employed to efficiently explore the parameter space and undertake sensitivity analyses. The integrated framework will be validated using analytical