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developing new machine learning methodologies that tackle unique computational problems in healthcare applications. We use large real-world complex datasets, including data extracted from electronic health
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to your expertise. What do we require? A PhD degree (or equivalent qualification) in AI (e.g., machine learning, natural language processing or computer vision); A strong scientific track record, documented
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, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the
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research, covering research topics including, but not limited to Crop Modeling, Machine Learning, Artificial Intelligence (AI), Internet of Things, and Big Data analysis for Precision and Prescriptive
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Consortium and host extensive supercomputing resources, including the "Cosmology Machine", some of which is part of the DiRAC national supercomputing facility. Further information may be found at http
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-renal diseases. Our research spans large-scale register and laboratory data, causal and predictive modeling, and computational image analysis of kidney biopsies. About the Research Group: Led by Professor
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for analysis of large-scale bulk and single cell data sets Strong understanding of statistical modelling, data normalisation and machine learning methods applied to biological datasets Experience with data
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and 10 research sections. We broadly cover digital technologies within mathematics, data science, computer science, and computer engineering, including artificial intelligence (AI), machine learning
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description and working tasks The project will develop privacy-aware machine learning (ML) models. We focus on data-driven models for complex and temporal data, including those built from synthetic sources
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. Collaborate on multidisciplinary projects involving high-throughput phenotyping platforms. Apply machine learning and deep learning techniques to improve image processing and trait prediction. Analyze large