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on state-of-the-art mass spectrometry-based proteomics of plasma, platelets, and endothelial cells combined with advanced cellular models. We offer a diverse and multidisciplinary research environment
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to predict traffic demand, user mobility, and network conditions, enabling autonomous decision-making that maximizes network performance and user experience. A unique aspect of this research is the integration
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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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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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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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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