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Field
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. The ML will use 500,000 fundus images from open-source and customised retinopathy datasets. We will compare retinopathy grading accuracy by NHS clinician vs ML algorithm. This will build on Exeter’s
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expand current technology to include automated live analysis, integrating machine learning algorithms capable of interpreting the complex behavioural patterns of mussels in response to environmental stress
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-weather perception for which Radar sensing/imaging is essential. This project focuses on developing algorithms, using signal processing/machine learning techniques, to realise all-weather perception in
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approach that integrates machine learning algorithms, blockchain technology, and IoT devices with digital twin systems. The scientific objectives of the project are as follows: Objective 1: Investigate how
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provide large and complex datasets. By applying advanced pattern recognition and clustering algorithms, the aim is to automatically detect coherent spatial domains. These domains represent regions with
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creating robust, low cost, and real-time edge-AI algorithms capable of accurately classifying diverse marine species and debris under complex and dynamic underwater conditions. The demand for such a low-cost
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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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having been used by humans and integrated the data with a global bivalve database of species traits, fossil occurrences and geographic distributions, setting the foundation for a forecasting framework
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Researcher will influence the direction of application areas and algorithm development, receiving direct training in InSAR processing, geospatial data science, and agricultural remote sensing. Co-supervision
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conduct cutting-edge research on topics including, but not limited to: Complexity theory Quantum algorithms and complexity Sublinear algorithms Interactive proofs, PCPs, and zero-knowledge proofs