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Field
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, reliability, and environmental resilience. The proliferation of intelligent systems has led to increased energy consumption, raising concerns about sustainability and operational costs. Energy-efficient
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to material, cutting tools and parts production. The PhD project will therefore focus on the development of an integrated system combining direct and indirect tool wear monitoring for reliable residual life
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gate drive implementations capable of maintaining reliable switching performance under cryogenic thermal conditions. This project will involve a substantial amount of experimental work using the high
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process. An integral part of the project will be the development of enhanced data-driven physics methods to achieve reliable prediction of material removal rate and material removal distribution
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reliability and operational efficiency. Determining the optimal size and location of PSTs within a network is inherently complex due to the nonlinear and dynamic nature of power systems, necessitating the use
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design that is applicable to different types of energy harvesters. This project will explore how PMC can be optimised to maximise efficiency, reliability and scalability using novel circuit topologies
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principles of project management. Ability to clearly formulate research results. Ability to present research results. diligence, responsibility, reliability, openness to change, team spirit, patience
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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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theory. While the GW method reliably describes photoelectric (main) peaks, it often fails to accurately capture satellite positions, which require more advanced techniques such as the cumulant approach (GW
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computational tools for predicting satellite features in XPS spectra of 2D framework materials. Your work will be based on the GW approximation within Green’s function theory. While the GW method reliably