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Cardiomyocyte Dynamic Network Analysis with Machine Learning (Ecidna-ML) This project represents a new approach to map dynamical interactions in networks of human cardiac cells. Network dyssynchronisation is a
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and progamming skills; Knowledge of Python.; Knowledge of Bayesian statistics, machine learning, and optimisation, or a willingness to learn. This scholarship is open to candidates of any nationality
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and provide a second opinion with a level of certainty to assist human experts. From the aspect of research methodology, the research work involves image processing, data analytics and machine learning
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interdisciplinary researchers to grow ideas from concept through to manufacture, instrumentation, and testing, alongside advanced computational mechanics, machine learning, and data analytics workflows. For more
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electronics and machine learning. Proficiency in programming languages such as Python, C/C++ and MATLAB. Demonstrated relevant project experience. Excellent analytical and problem-solving skills. Due to funding