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team bridging aerospace and materials research, with access to high-performance computing resources and the university’s state-of-the-art materials research facilities, including high-resolution X-ray CT
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well as cutting-edge research insight. Description The global drive towards electrification in high-performance sectors such as motorsport and aerospace is pushing electric motors to operate at ever increasing
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the development of system software. Key questions include how LLMs can support programmers in writing complex logical code, generating high-quality tests, and optimizing performance. Moreover, when integrated with
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spatiotemporal coupling (STC) at the far-field Design optical elements to generate a specific RDG and STC, e.g. doublet lens including axi-parabola Perform laser wakefield acceleration using "flying focus
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holography, combined with ultra-fast laser excitation and ultra-short electron pulses, to monitor carrier dynamics. The goal is to identify and map regions of high quantum efficiency as well as recombination
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PhD Position in Theoretical Machine Learning – Understanding Transformers through Information Theory
performance in core mathematics and machine learning courses Master’s degree (or near completion) corresponding to at least 240 higher education credits in mathematics, computer science, electrical engineering
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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on Artificial Intelligence (AI), Deep Reinforcement Learning (DRL), and Predictive Maintenance for optimizing wind turbine performance and reliability. This research will develop an AI-powered wind turbine
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PhD Studentship: Multi-robot Cyber-physical Systems for Solar Farm Health Monitoring and Maintenance
connectivity requirements for ensuring reliable and seamless communication in managing a coordinated robot fleet. Connect across the robot-centric network to ensure high-fidelity synchronisation while performing
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into practical performance solutions. The “Travel, sleep and fatigue optimal management for long-haul performance” PhD programme will focus on mitigating circadian and travel fatigue challenges associated with