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system using deep learning (DL). The project’s objectives include generating training data from synthetic datasets and real-world images (cadaver and actual intraoperative THR images), developing a marker
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) Desirable: Experience with video processing and transformer architectures Knowledge of multimodal learning approaches Interest in accessibility technologies Interest in learning new spoken and signed
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capabilities needed for truly sustainable operations. Research Question: How can AI-enhanced digital twin technologies with advanced optimisation algorithms transform manufacturing processes to achieve
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Your Job: Reinforcement Learning (RL) is a versatile and powerful tool for control, but often data-inefficient, requiring numerous updates and non-local information such as replay buffers and batch
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to compensate for such aberrations, significantly enhancing image quality. Adaptive requires knowledge of the wavefront to be corrected. Our team has been developing a machine-learning approach to wavefront
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) or Machine Learning models. These tools will be integrated with physics-based models of environmental loading (waves and wind) to enhance the accuracy and robustness of the assessment. All components assembled
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nanomaterials synthesis and characterization is desirable. Interest in energy storage technologies and machine learning applications in materials science. Strong analytical and problem-solving skills, and the
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(e.g., Kalman Filter) or Machine Learning models. These tools will be integrated with physics-based models of environmental loading (waves and wind) to enhance the accuracy and robustness
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of multicore fibre (MCF) technology.MATCH (Multicore Fiber - Applications and Technologies - Match) is a Marie Sklodowska-Curie doctoral network funded by the European Commission under the Horizon Europe. MATCH
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and technology. Engaging in solving a real-world problem requires a real solution to enhance housing resilience. Applying research skills to optimise and innovate AI-driven solutions for the housing