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paraganglioma driven by cell plasticity using spatial transcriptomics and machine learning.” High-risk neuroblastoma (NB) and malignant paraganglioma (PPGL) are neural crest–derived tumors with pronounced
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap
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protocols, ITC will focus on the monitoring and response parts, building on many earlier projects revolving around the use of UAV/drones, computer vision and machine learning, change and damage detection, and
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exposed to Bayesian optimization to find the optimal set of parameters that improve process performance and material quality. Secondly, different machine learning strategies based on traditional supervised
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by detecting and predicting threats such as pests, diseases, and environmental stress in line with the UK Plant Biosecurity Strategy. The project harnesses computer vision, deep learning, and large
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the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real
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AI systems are reshaping how we learn, work and participate in democracy, our centre tackles the promise and peril of hybrid intelligence—human and machine working and learning together. AI LEARN’s
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qualifications You have graduated at Master’s level in computer science, computer engineering, human-computer Interaction, media technology, visual learning and communication, or closely related fields
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of parameters that improve process performance and material quality. Secondly, different machine learning strategies based on traditional supervised learning techniques (e.g. random forest (RF
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the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real