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Field
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-seq datasets, and applying advanced statistical and machine-learning methods (AI/ML) to extract novel biological insights that drive our translational and fundamental research programmes. In
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population genetics, bioinformatics, computational biology, statistics or probabilistic machine learning and computer science. Experience of working with large genotyping or sequencing data sets A proven
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embedded AI systems. They will demonstrate a strong track record of high-quality research in machine learning/AI and/or embedded systems, evidenced by publications in leading conferences and journals
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the ability to develop novel theory. They must also have strong development skills, to enable them to lead the process of prototyping new interactive systems with sensors, build machine learning
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Applicants are invited for the posts of Research Associate or Research Fellow in Machine Learning to work with AI Researchers in the Centre for AI Fundamentals at the University of Manchester. You
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algorithmic foundations of quantum adversarial machine learning, an emerging field at the intersection of quantum computing and machine learning. It investigates how the unique capabilities of quantum computing
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tools such as R, Python, or MATLAB as well as relevant machine learning frameworks Experience in statistical data analysis, and expertise in areas such as experimental design, linear/nonlinear models
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modelling, satellite data assimilation, multivariate statistics, and machine learning. Prior experience with model and satellite products for mapping and understanding SM-dependent hazards (like floods
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machine learning/AI or cybersecurity, or both, as evidenced by a strong track record of publications in leading journals and conferences in relevant areas. Software programming skills. To be successful
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and machine-learning methods (AI/ML) to extract novel biological insights that drive our translational and fundamental research programmes. In addition to your research leadership, you will play a