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advantages for manipulation and locomotion, but current control algorithms do not fully exploit their capabilities. Most rely on approximations tailored for rigid systems or require extensive sensing and
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new business models based on distributed storage and dynamic sharing coefficients, integrated into a regulatory sandbox and real-world test environment. Main tasks: Create algorithms to maximize
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learning algorithms, and design of optical communication networks or power consumption and energy saving. The synergies of MATCH consortium act together to enable the thirteen DCs to become the next
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operate safely around humans. They offer unique advantages for manipulation and locomotion, but current control algorithms do not fully exploit their capabilities. Most rely on approximations tailored
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are searching for a motivated PhD candidate to design practical over-the-air computing algorithms and protocols for future edge AI applications. About the employer The research of this PhD position will be
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of prior data?) Additional research topics may include: Algorithmic Transparency and Fairness in Funding Decisions Comparative Analysis of Funding Models AI-Driven Predictive Analytics for Funding Success
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You will join the EPSRC-funded project “Behavioural Data-Driven Coalitional Control for Buildings”, pioneering distributed, data-driven control methods enabling groups of buildings to form
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modelling and simulation of transmission and distribution networks, including benchmarking data models, developing optimal power flow algorithms, and creating state estimation and multi-energy optimisation
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emerging types of national emergencies and evaluate their spatial and operational implications. This will include an analysis of UK population distributions, terrain, infrastructure access, and airspace
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A PhD studentship is available to work on Logistics automation. The student associate will work in the Intelligent Logistics Group within the Distributed Information and Automation Laboratory (DIAL