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of video and low-cost sensor technologies to capture subtle movement patterns, creating a rich dataset for AI-driven analysis. Machine learning, deep learning, computer vision and multimodal AI methods will
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deep-learning and 3D computer-vision models that detect features while representing a distribution of plausible interpretations. Encode geological relationships in a knowledge graph that stores
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experiments and policy-capturing methods may be used to compare interviewer judgements with evidence-based outcome measures. The project will also explore machine-learning, multimodal data analysis, computer
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backgrounds such as AI, computer vision, computer graphics, machine learning, robotics, wearable technologies, textile engineering, fashion technology, digital fashion, or related areas are encouraged to apply
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, relating to craniofacial identification research and machine learning. You will require a computer science background. You will be applying AI and/or machine learning to Face Lab processes in relation
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directly with speaker communities to ensure the technology is genuinely useful to them. You will gain deep expertise in machine learning and natural language processing, access to high-performance computing
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integrating power electronic converters and electrical machines we can use common structures and systems to greatly reduce, material usage and energy consumption. Through a multidisciplinary research approach
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systems are generally ill-conditioned. The project sits at the intersection of classical numerical analysis, scientific machine learning and computational chemistry. Based on regularization techniques and
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for candidates with a background in or demonstrated ability to learn about: Bayesian methods, probabilistic machine learning or inverse modelling. Prospective applicants are encouraged to direct informal inquiries
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on clinical complications, and use machine learning to develop and validate predictive models to identify high-risk patients. The research aims to individualise inpatient care, reduce hospital-acquired