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Funding for: UK/Home Students We invite applications for a fully funded PhD research scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available
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/Data Science, Statistics or a related field. Experience with statistical analysis, including but not limited to regression analysis and machine learning. Self-motivated, with an ability to drive projects
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, including in computational tasks where data visualization, preprocessing, or interpretation can be improved. Devises and deploys custom machine learning approaches where applicable. Provides feedback and
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parallel processing, FPGA coding and analysis, along with Machine Learning and AI based image analysis. The final aim of the project will be to generate in-situ / live film profile data to coating line
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experience to apply those skills toward imaging data (e.g., live cell microscopy). The successful candidate will contribute to advancing machine learning-driven analysis of high-content imaging data to achieve
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; some college coursework in a health, biological, or data-related field preferred. Basic computer skills, including familiarity with Microsoft Excel or Google Sheets. Strong attention to detail and
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or veterinary contexts. Strong foundation in computer vision and machine learning frameworks (e.g., PyTorch, TensorFlow, OpenCV); experience with video annotation tools and datasets. Hands-on experience with
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push the boundaries of neural scene representations in a medical imaging context. The successful candidate will work alongside a multidisciplinary team of deep learning researchers, computer vision
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About Us We are seeking experts in medical image deep learning to join our team and help develop novel computationally efficient segmentation algorithms. We welcome application from individual with
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deep learning to join our team and help develop novel computationally efficient segmentation algorithms. We welcome application from individual with experience in: Deep learning Medical imaging computing