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for Quantum-enhanced, distributed radar signal processing techniques that maximise target Signal-to-Noise Ratio (SNR). The algorithms we will derive will be applied and tested in the context of target detection
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action discretization, exploration parameters, and decision intervals significantly affects algorithmic convergence and system performance. Distributional RL techniques, such as quantile regression and
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as soon as possible but must be available to start by 1 April 2026 at the latest. This project aims to develop superconducting microwave interconnects and metasurfaces for distributed quantum networks
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between things, developing the mathematical and computational foundations for optimal information transfer between surfaces and volumetric current distributions in complex scattering media. The research
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, data-limited, and dynamically evolving environments. Applications include complex engineered systems such as intelligent communication networks, distributed computing platforms, and quantum-enhanced
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PhD Studentship: Distributed and Lightweight Large Language Models for Aerial 6G Spectrum Management
resources. To fill this gap, this proposal aims to design novel distributed and lightweight LLMs for spectrum management in aerial 6G networks. Specifically, the project will design wireless-aware data
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algorithms * Parallel algorithms and distributed computing * Parameterized complexity and structural graph theory * Random structures and randomized algorithms * Sublinear and streaming algorithms
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causal analysis across distributed datasets while preserving privacy. The successful candidate will be responsible for the end-to-end investigation of novel federated learning strategies for causal
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-cases of classical supercomputers, the development of quantum CFD algorithms will be of widespread benefit upon the arrival of fault-tolerant quantum computing. This project involves the adaptation
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-scale Logistics. Our vision is that local production, distribution, and reuse of goods using robot swarms will enable a more sustainable future through reduced transport emissions and waste. This vision