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Algorithm Developer (KTP Associate) ( Job Number: 25001803) Department of Mathematical Sciences Grade 7: - £41,064 - £46,049 per annum Fixed Term - Full Time Contract Duration: 18 months Contracted
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candidate will work on algorithm development for contact-rich robotic manipulation, integrating perception, planning, and control within a real-world robotic system. Job Responsibilities: Develop, implement
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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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-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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PhD project: Modelling Resilience of Water Distribution Networks Supervised by Rasa Remenyte-Prescott (Faculty of Engineering) Aim: To develop an modelling approach for assessing water network
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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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developing the theoretical and algorithmic foundations of compositional world models. A key application focus of the grant lies in rapid and safe real-world skill acquisition in application domains such as
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Almost all radar systems currently transmit from the same location. A drastic departure from this sensing architecture is distributed radar – enacted by a coherent network of spatially distributed
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The Leverhulme Centre for Algorithmic Life is a major new interdisciplinary research hub dedicated to the study of how machine learning and AI algorithms are transforming societies. The Centre is
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at scale raises privacy and real hardware constraint concerns. This PhD will focus on those challenges by developing a distributed, privacy-preserving NILM framework, so we can move from small research