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PhD studentship: Machine Learning-driven Fusion of Drone-mounted Ground-penetrating Radar and Smart Meter Data for Advanced Leak Detection in Water Networks Award Summary 100% fees covered, and a
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of dehydration using a low-power radio-frequency (RF) sensor. The research objectives include design optimization to improve wearability, robust data acquisition using machine learning and establishing correlation
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; EPSRC Centre for Doctoral Training in Green Industrial Futures | Edinburgh, Scotland | United Kingdom | 6 days ago
, further exploring the carbon-related impact of polymer breakdown. The project will also incorporate artificial intelligence (AI) and machine learning to optimise degradation processes and data analysis
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untapped potential remains in extracting value from this data. This PhD will explore advanced analytics techniques, including machine learning, digital twin modelling, time series analysis, spectral analysis
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This PhD project aims to advance Safe and Sustainable by Design (SSbD) pharmaceutical manufacturing by integrating cutting-edge methodologies, including computer-assisted retrosynthesis, end-to-end
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to begin focused research early on. To complement their background, optional training in natural language processing, machine learning, and linguistics will be available. During the first year, the student
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, adaptive control strategies, and hybrid energy storage solutions to address key challenges in self-powered systems under dynamic environmental conditions by: Develop machine learning or heuristic-based
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specific drug resistance and pathogenesis mutations. The project will combine classical microbial genomics with machine learning and AI analysis approaches to create the most in depth population analysis
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Title: Predicting and Improving the Quality of Recycled Plastics Using Advanced Metrology and Data Science Research theme: "Materials Characterisation" "Data Science and Machine Learning in
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formats available in conventional hardware are often too accurate for the needs of machine learning: they do not improve the quality of the trained model but may deteriorate it by causing overfitting