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. Develop new machine learning methodologies (from artificial neural networks, decision trees, evolutionary algorithms and others) compatible with epidemiology. Produce a digital twin for national suicide and
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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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and reliability. You will integrate, adapt and develop methods (using packages such as BioSPPy) for the processing and analysis of physiological signals measured for determining deception. The project
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designed to meet multiple needs in marine biodiversity monitoring. The project aims to develop embedded novel deep learning and computer vision algorithms to extend the system’s capabilities to classify
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-Making and Route Optimisation: Develop adaptive algorithms within a bias-aware ensemble Kalman filter framework to propose alternative flight paths dynamically. The system will aim to maximise safety and
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tuition fees. This PhD project in the area of autonomy, navigation and artificial intelligence, aims to advance the development of intelligent and resilient navigation systems for autonomous transport
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with symptoms. However, our brain operates differently between sleeping and waking brain states, and an optimal system should take this into account. The aim of this project is to develop brain state
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. Analysis of images will investigate the efficacy of manual digital approaches (e.g., Dot Dot Goose) and the development of a marine litter characterisation and quantification algorithm for automated analysis
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samples. All computational methods and algorithms will be implemented as part of the python based MetaboLabPy platform (https://doi.org/10.3390/metabo15010048 , https://github.com/ludwigc/metabolabpy
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GNSS alone leaves systems vulnerable to interference, spoofing, or outages, particularly in dense urban environments. The development of 6G networks with integrated TN and NTN infrastructures provides