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Field
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traffic demands increase, there is a growing need for innovative methods to continuously assess track condition and predict deterioration. This PhD project addresses this challenge by developing a novel
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are looking for a motivated and talented PhD candidate to join a unique interdisciplinary project at the intersection of machine learning and formal methods. Information Machine learning models deployed in real
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, including efficiency, fairness, reliability, and sustainability. Despite recent advances, existing mathematical models often fail to jointly capture these aspects and trade-offs, limiting their ability
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, reliability, and operational performance. As railway infrastructure ages and traffic demands increase, there is a growing need for innovative methods to continuously assess track condition and predict
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empirically test a Three-Channel Interview Assessment Model combining: verifiable attributes such as qualifications, work samples and assessed skills; self-reported information such as experience, motivations
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in development to ensure the materials are safe, affordable, and user-friendly. The project will also explore behavioural drivers, incentives, and innovative business models to stimulate adoption
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genetic perturbations into predictive models of tissue self-organization and repair. The project offers comprehensive interdisciplinary training in computational developmental biology and the opportunity
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performance. These models will be combined into a multi-physics framework capable of predicting reactor behaviour during normal operation and transient conditions. You will validate simulation results using
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integrated early in development to ensure the materials are safe, affordable, and user-friendly. The project will also explore behavioural drivers, incentives, and innovative business models to stimulate
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-order predictive models. The student will gain hands-on experience in industrial applications, including practical aspects of aeroengine oil system design, spending part of their PhD based on-site