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Industrial cybersecurity, supply chain security Semantic modelling of industrial artefacts, knowledge representation and automated reasoning Your supervisor will be Associate Professor Andrei Lobov . Your
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. Experience with optimization methods, numerical modeling, or simulation of complex systems. Experience with 3D modeling, CAD APIs, or computational geometry is an advantage. Experience and abilities
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– and building on recent advances in foundation models, neural model predictive control, and robotic world models – this PhD project will investigate principles and mechanisms for a shared autonomy
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of the research project are (i) Identify different climatic areas relevant to the study and characterize weather conditions to be used as boundary and initial conditions in modelling; (ii) Develop thermo-hydro
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? Can the experimentally observed PD properties, decomposition products and memory and ageing effects be explained using known theory and computational models? The main supervisor will be Professor Frank
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use the model species thale cress (Arabidopsis thaliana) as a resource to help identify the molecular mechanisms and genes underlying responses to altered temperatures and parasite (co-)infection. We
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cell walls, which have been implied in responses to the two parasites. We will also use the model species thale cress (Arabidopsis thaliana) as a resource to help identify the molecular mechanisms and
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simulations can support data augmentation. Key deliverables of this project will be a multi-target radar tracker that learns to utilize radar targets’ amplitude signatures for data association and state
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models to enhance learning through AI technology. The PhD fellow will engage with developing and evaluating models and agents, as well as, multi-agent networks that support the human learning and improving
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an efficient and automatic manner, and how simulations can support data augmentation. Key deliverables of this project will be a multi-target radar tracker that learns to utilize radar targets’ amplitude