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of short-axis MR image sequences. Training You will be based at the Vision Computing Lab within the School of Computing Sciences, which specializes in deep learning for medical image analysis and neural
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methodology to generate confidence in such decision, potentially reducing maintenance costs and down-time for offshore wind energy production. The images taken by each drone are loaded into the pre-processing
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marine sciences, biological oceanography, ecology, or computer sciences. Strong analytical, numerical and practical skills are essential. Experience in coding or applying quantitative methods in a
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(computer vision technologies). The interdisciplinary nature of this PhD will require the integration of environmental science, engineering, and community science methodologies. Supervisors: Primary
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quality, diversity, and biological relevance using standard metrics and expert review. Anonymised digital images from tissues in biobanks will be used to train generative models on university computing
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imaging and monitoring of resin infusion in sample composite structures to build the digital twin and then explore methods for defect prediction and real time process control. The improved process
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and technology. Desirable (not essential): Research experience in sports medicine and technology (e.g. publication or final year project). Programming skills in MATLAB or Python. Application Procedure
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settings, while providing essential process insights (such as electrode spacing, solvent chemistry, and operational voltage/current conditions) to support further technological development. Project specific
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include training in computer science, human-computer interaction, etc. A Master’s degree is desirable. In addition, candidates for whom English is a second language should meet the University’s minimum
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focus on the medical image processing aspect of the Birth4Cast simulator by researching and developing automated image segmentation procedures to extract the pelvic floor muscle complex and the fetal head