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Field
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explore multimodal learning and unlearning techniques using vision, language, and audio signals to build intelligent systems capable of interpreting and responding to human actions and emotions. This work
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methods for load identification and modelling to infer load behaviour from measurements at the grid supply point (GSP). Your work will help determine whether new load types need to be defined in the CLM
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and reliability. You will integrate, adapt and develop methods (using packages such as BioSPPy) for the processing and analysis of physiological signals measured for determining deception. The project
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modelling to infer load behaviour from measurements at the grid supply point (GSP). Your work will help determine whether new load types need to be defined in the CLM framework to accommodate new components
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environment Proven ability to use a scientific programming language such as python or MATLAB for signal processing A desire to improve therapies available to patients with neurological conditions Excellent
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performance simulation capabilities for gas turbine engines developed at Cranfield University as the starting point. Applications are invited for a PhD studentship in the Centre for Propulsion and Thermal Power
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Research theme: "Next Generation Wireless Networks", "Signal Processing", "Machine Learning" UK only How to apply: uom.link/pgr-apply-2425 This PhD project aims to design novel resource allocation
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the electrical contacts and magnetic field to influence the fault current interruption process. The PDRA will contribute mainly to the arc modelling part of the project and play an active role in organising and
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PhD Studentship: Distributed and Lightweight Large Language Models for Aerial 6G Spectrum Management
-latency, and scalable operation in aerial 6G networks. In this regard, Large Language Models (LLMs) have recently emerged as a key technology to achieve adaptive 6G spectrum management. The core idea of LLM
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crucial for ensuring grid stability and economic operation in future high, medium transmission and low voltage power distribution networks. This integration introduces significant challenges in voltage and