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(Edge AI) enables deploying AI algorithms and models directly on edge devices. However, AI workloads demand high performance processing, large scale data handling, and specialized hardware accelerators
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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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Skills/Background The applicant should have a solid background in computer science or systems engineering. Knowledge of AI/ML algorithms and simulation environments is highly advantageous. A keen interest
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, delivering greater performance, functionality, and reliability. This demands the adoption of faster switching wide bandgap devices and greater system integration. About This PhD This PhD programme is part of a
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) sensor data. This will be a small system-on-chip designed to operate on the edge (i.e. close to the sensor). The project will explore whether emerging logic-based ML algorithms can be translated
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to operate on the edge (i.e. close to the sensor). The project will explore whether emerging logic-based ML algorithms can be translated into smaller, faster, more energy efficient and cost-effective hardware
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simulate hydrodynamic and pollutant transport processes, their computational cost limits their suitability for real-time emergency decision-making. This project addresses this challenge by combining physics
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pressures from climate change, urbanisation and ageing infrastructure. Although high-fidelity numerical models can simulate hydrodynamic and pollutant transport processes, their computational cost limits