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This self-funded PhD research project aims to advance the emerging research topics on physics-informed machine learning techniques with the targeted application on predictive maintenance (PdM
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the electricity generation mix continues to grow. Installed capacity in the UK in 2020 was 13.4 GW and is expected to increase to 40 GW by 2030. Accelerating the adoption of solar energy will present significant
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. Simulations are suitable to characterise processes in healthy and diseased individuals including stroke patients. Machine learning methods might be considered to accelerate simulations. The project provides a
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accelerate the world’s transition to carbon-neutral energy systems? Join the Thermofluids Group in the Department of Mechanical Engineering at the University of Sheffield, and embark on a transformative PhD
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and accelerate aviation decarbonisation efforts from various roles in industry, academia, government, and policy. The interview process is composed of two interviews. Following a first introductory
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of deep learning models, especially when new training experiences are corrupted. The framework will be validated in robotic control scenarios during EV battery assembly, under process variations such as
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research which combined efficient optimization and sequential reliability assessment. The project is funded through an EPSRC call to accelerate research outcomes to achieve a prosperous net-zero and is
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electrodes hold significant promise for improving bioelectronic systems, enhancing energy generation and storage, and accelerating the adoption of renewable energy applications. We invite applications for a
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industrial settings. From a practical standpoint, new predictive modelling approaches are needed to inform and accelerate industrial process design, as this is an area where much process development occurs
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techniques and advanced sampling methods to bring a significant advancement in reducing high-fidelity runs to accelerate the engineering design, validation process and improve the robustness of the prediction