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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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prediction, most current models still describe proteins largely as static structures and do not fully capture the conformational ensembles that underlie protein function. This PhD project aims to address
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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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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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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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, 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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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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mental models, cognitive maps, and cognitive graphs. These approaches have provided important insights into how people perceive locations, learn route layouts, and understand spatial relations. However
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objective is to develop methods that move beyond correlation-based prediction toward causal reasoning, intervention-aware modelling, and interpretable AI systems. This transition from correlation to causation
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. At the Division of Systems and Control , we develop both theory and concrete tools to design systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and