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science or systems engineering. Knowledge of AI/ML algorithms, particularly graph neural networks and reinforcement learning, is highly advantageous. A keen interest in distributed computing, IoT architecture, and
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support massive Internet-of-Things (IoT) deployments through technologies such as network slicing. At the same time, the emergence of large-scale quantum computing poses a significant threat to current
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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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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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pressures from climate change, urbanisation and ageing infrastructure. Although high-fidelity numerical models can simulate hydrodynamic and pollutant transport processes, their computational cost limits