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Field
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explore the link between weather predictions and resulting energy usage forecasts on different time scales and the impact of the uncertainty around those e.g., on sub-seasonal time scales ( Join our
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on methods development in machine learning, uncertainty quantification and high performance computing with context of applications from the natural sciences, engineering and beyond. It is embedded in
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decisions regarding the technological obsolesce of telecoms under real-world uncertainties. Benefits of joining this project: This project will give an opportunity for the student to explore the area of
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system can reduce dependence on fossil fuels – but at the same time brings new challenges and uncertainties. Disruptive events and changing political, social, and technological conditions can have a
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infrastructure to better predict and manage stormwater dynamics under climate-related uncertainty. It will also explore how to optimize design and operation strategies to adapt to future climate extremes. Possible
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like population growth, climate uncertainties and sustainability transitions. To plan resilient urban spaces that promote social cohesion and well-being in an inclusive way, new methods are needed
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develop probabilistic guarantees that quantify uncertainty in human preference alignment while ensuring robustness against adversarial conditions. The ability to mathematically verify AI alignment has
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and machine learning to tackle the complexity of force allocation and motion planning under uncertainty and actuator failures. The project combines theoretical research in stochastic optimal control
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, embedded intelligence, and adaptive cyber-physical systems that operate safely under uncertainty and dynamic conditions. This PhD at Cranfield University explores the development of resilient, AI-enabled
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regulations and product certification due to the inherent uncertainty of how AI systems make decisions. Classical engineering development guidelines, are difficult to interpret or simply not transferrable to AI