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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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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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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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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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techniques to design new and efficient algorithms that can provide strong protection during the entire life cycle of ML models used on the devices. Research Task I: Investigate ML algorithms and optimisations
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experiments for months before the value of output y is measured for some given input x. This creates an exciting challenge for AI researchers to develop smart algorithms that can find the optimal value of input
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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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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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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