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package should be prioritised are surprisingly difficult computational tasks. State-of-the-art high-performance algorithms are used to calculate routes for the vehicles in order to minimise costs and
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abdominal symptoms. Standalone clinical indicators like CA125 or traditional screening algorithms (e.g., ROMA) frequently produce ambiguous results or lack sufficient sensitivity for early detection
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This research aims to design a sustainable framework for optimizing distributed computing systems to enhance performance while minimizing energy consumption. Existing scheduling algorithms often
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computation is necessary before taking an action. This PhD project will address these challenges by developing trustworthy and resource-adaptive VLA models. A central question is whether an embodied agent can
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human-centred embodied AI New methods for modelling human behaviour and interaction dynamics Multimodal temporal and predictive learning algorithms Experimental evaluation against strong contemporary
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This PhD project focuses on the design and evaluation of hybrid quantum–classical algorithms for large-scale data analytics and optimisation problems. The research will investigate how quantum
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that can better utilise distributed computing resources while reducing energy overheads and supporting more sustainable AI infrastructure. The successful candidate will have the opportunity to work on real
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Optimisation methods, such as mixed integer linear programming, have been very successful at decision-making for more than 50 years. Optimisation algorithms support basically every industry behind
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management, distributed computing, and energy-aware computing, preparing them for impactful roles in industry and research. Key Components and Example Scenarios Predictive Resource Allocation and Load
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Anomaly detection is an important task in data mining. Traditionally most of the anomaly detection algorithms have been designed for ‘static’ datasets, in which all the observations are available