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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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are currently seeking applications for a Postdoctoral Research Associate in all algorithmic areas of Theoretical Computer Science. In this role you will work closely with Drs Wirth, Mestre and Canonne
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Associate in all algorithmic areas of Theoretical Computer Science. In this role you will work closely with Drs Wirth, Mestre and Canonne on algorithmic research projects, and contribute to the collegial
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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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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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underwater robots. - Designing, implementing, and evaluating navigation, control, and mission planning algorithms. - Studying multi-robot coordination and human-robot teaming strategies. - Participating in
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algorithms to achieve real-time control and process optimisation of drainage pipe networks. It seeks to improve the operational efficiency of urban wastewater systems, reducing energy consumption and extending
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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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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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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