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materials, to aid design of novel more energy-efficient processing routes. The development of these digital twins requires reliable and predictive models for microstructure formation during steel processing
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health management (IVHM) system that leads to enhance safety, reliability, maintainability and readiness. Generally, prognostics models can be broadly categorised into experience-based models, data-driven
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the interface between different scientific disciplines including ecology, evolutionary biology, mathematics and statistics, informatics, economics and social sciences. We aim to apply advanced statistical and
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for electric machines to address challenges of increasing efficiency, reducing weight, volume, cost, redundancy, and reliability. The candidate will prioritize industry requirements in designing, development and
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computers, one of the major milestones is the development of high-quality quantum bits (qubits), the core units of quantum computation. Unlike classical bits, solid-state qubits must operate at extremely low
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model's performance across different patient groups, including varying ages and comorbidity profiles. (b)Gather feedback from clinicians to refine and improve the model's utility in clinical practice
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, for static and dynamic measurements and reliability measurements). Key Responsibilities Research & Learning: Develop expertise in GaN device physics and wide-bandgap semiconductor technology. Simulation
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Machine Learning-based diagnostics and prognostics digital twin system will be developed, aiming to provide fast and reliable predictions of the health of gas turbine engines. Non-confidential operational
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, the internal workings of deep neural networks remain largely mysterious, posing a significant challenge to the interpretability, reliability, and further advancement of these models. This project seeks deep