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of the nonlinear structural performance utilizing reliable and computationally efficient numerical structural models. To support the condition (state) assessment, you will also explore the use of advanced estimators
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hardware is developed in cooperation with Heidelberg university. Data analysis, writing and publishing research articles, and presenting at topical conferences/workshops are an integral part of this project
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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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sensing information from key locations on the offshore infrastructure. In this regard, the research will focus on the assessment of the nonlinear structural performance utilizing reliable and
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simulations and finite element analysis, with high-heat flux electron beam experiments. The research will simulate and replicate steady, cyclic, and transient thermal loads to better understand PFM behaviour
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. Experience writing project proposals. Experience in tomography analysis for 3-D imaging. * Significant experience is defined as two (2) to five (5) years of experience. Condition of Employment Reliability
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application-oriented research that unlocks the potential of data through rigorous analysis – advancing solutions in societally relevant domains. Your profile Master’s degree in Statistics, Industrial
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focus more on model development, robustness, and long-term reliability. What you can expect Modelling. Apply probability theory, statistical analysis, and machine learning techniques to build robust
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supported by collaborations with industry giants including Boeing, Rolls-Royce, Thales, and UKRI, this research offers a unique platform to contribute to the advancement of secure, reliable, and transparent
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into areas such as AI-driven verification, predictive maintenance, and compliance assurance, aiming to enhance system reliability and safety. Situated within the esteemed IVHM Centre and supported by