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scintillator-based radiation sensors combining multiple materials with complementary functions, offer a promising route to overcome these limits and achieve unprecedented timing resolution (sub-70ps), enabling
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across multiple sectors.
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—remains a critical challenge. This project will focus on designing AI-driven cognitive navigation solutions that can adaptively fuse multiple sensor sources under uncertainty, enabling safe and efficient
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technically sophisticated and genuinely user-friendly. This project aims to develop a comprehensive AI-driven software platform for smart sanitation systems that serves multiple user groups through tailored
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team Work across multiple departments and areas, including but not confined to: Food & Beverage service, Guest Care Crew – conference & events, Housekeeping, Kitchen and Reception Experience in some of
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reusable launchers, autonomous robotics, and advanced materials could redefine how we design space structures. The ability to remotely assemble orbital systems from multiple launcher payloads would allow
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flow visualisation and measurement techniques to study droplet impact under icing conditions to improve icing codes that aid in design and development of ice detection and mitigation system
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analytical frameworks grounded in Mean Field Game (MFG) theory and Multi-Agent Reinforcement Learning (MARL), which are tailored for eCPS. These frameworks will facilitate the creation of effective control
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—remains a critical challenge. This project will focus on designing AI-driven cognitive navigation solutions that can adaptively fuse multiple sensor sources under uncertainty, enabling safe and efficient
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planning algorithms to re-route or schedule multiple vehicles to minimise the impact on the efficiency and safety. This PhD position is related to a 2-year project funded by SESAR, involving various partners