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guarantees of FL. In this project, we aim at an ambitious goal - designing secure and privacy-enhancing algorithms and framework for FL and applying our designs into real-world applications. To achieve
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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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Modern map-based systems and location-based services rely heavily on the ability to efficiently provide navigation services and the capability to search points of interests (POIs) based
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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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-driven reasoning—crucial for optimising navigation, resource discovery, and logistics—remains underexplored. This project aims to address key challenges in this domain, including interpreting complex map
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through theory and simulation and/or experimental design and testing; developing new image reconstruction algorithms for providing more information with less radiation; and applying our techniques
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healthcare application needs to analyze sensitive patient data across distributed nodes. Researchers and students can explore privacy-preserving algorithms and technologies like federated learning and zero
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broad range of topics: from model-predictive building control and community battery integration to wind farm optimisation and multi-decade investment planning, we support clever algorithms and data
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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
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learning, algorithms, and programming. Prior exposure to reinforcement learning or human-robot interaction is highly desirable, though motivated candidates with a strong grounding in AI/ML and willingness