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(e.g., model compression/simplification and hardware-aware optimization). We are also interested in how resource-efficiency interacts with broader sustainability aspects of machine learning such as
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and interpretability, analogous to RAG (Retrieval-Augmented Generation) in LLMs Investigating methods for improving AI model sustainability, e.g. model compression techniques (such as quantization and
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Rolls-Royce UltraFan has completely changed the architecture of the compression system. This has opened the design space and means that new technologies that can improve performance and reduce fuel burn
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statistics This PhD project falls under the collaboration between Research Thrust RT2 Physics-based models, and Research Thrust RT3 on representation, compression, learning, and inference. For long-distance
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on constrained platforms using techniques such as model compression, quantization, and hardware-aware neural network design. Investigating mechanisms that protect the integrity and reliability of deployed AI
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and servers such as gradient compression, asynchronous training, reduced synchronization frequency, semantic communication, and design of new application and transport layer protocols. Data management
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lightweight AI models suitable for real-time execution on constrained platforms using techniques such as model compression, quantization, and hardware-aware neural network design. Investigating mechanisms
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of a delamination can seriously reduce the strength and stiffness of a laminate especially under compressive buckling loads, potentially leading to catastrophic failure. We have developed new generation
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model pre-training and multimodal adaptation to architectures and compression for edge deployment while targeting real-world validation in domains like HealthTech, smart industry, and autonomous mobility
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inference and deployment costs (e.g., model compression/simplification and hardware-aware optimization). We are also interested in how resource-efficiency interacts with broader sustainability aspects