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insights for streaming, broadcast, accessibility and media production. Candidate profile Applicants should have a background in machine learning, audio engineering, speech processing or a related discipline
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. Experience of working with large multimodal datasets. Interest in human-computer interaction and human-centred system design. Strong communication and organisational skills. While it is not necessary to have
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be a game changer. Deep learning models can learn the mapping between material states and ultrasonic responses from simulation data, delivering quantitative predictions once trained, and remarkably
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modelling, machine learning, or microfluidics. They will also have excellent communication, organisational and problem-solving skills, and a strong interest in interdisciplinary quantitative biology
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. Experience of working with large multimodal datasets. Interest in human-computer interaction and human-centred system design. Strong communication and organisational skills. While is not necessary to have
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, and international studies—with cutting-edge data science techniques, including Earth Observation (EO) data analysis, machine learning, large-scale collation and analysis of survivor narratives
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missing and incomplete symptom data can introduce bias and worsen existing inequalities. This PhD will develop innovative statistical and machine learning approaches to understand, model, and overcome
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health. Successful candidates may have experience in electron or X-ray microscopy, image analysis, AI and machine learning, quantitative data science or computational modelling. They will be able to work
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About the project: Extreme Weather Storylines via Generative AI Supervisor: Dr Tobias Grafke, University of Warwick Generative machine learning techniques, as known for example from large language
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About the project: Machine learning accelerated electronic transport calculations for complex materials Supervisor: Prof. Neophytos Neophytou, University of Warwick Advancements in materials