20 programming-"https:" "https:" "https:" "https:" "https:" "Data driven Materials Modeling" research jobs at University of London
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About the Role This postdoctoral position supports a Cancer Research UK-funded research programme investigating epithelial cell heterogeneity during the initiation of pancreatic cancer, with a
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Council-funded programme grant. This will involve in-depth analysis of the interface between Klebsiella and the gut microbiome, leading to the discovery of microbiome consortia with enhanced colonization
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or regulatory decision-making. They will have strong knowledge of machine learning research methods, excellent programming skills, and the ability to communicate complex ideas clearly to academic and non
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research and methodological development experience. Candidates will need to have significant experience programming statistical methods, preferably in R, as well as experience handling missing data using
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, a major regional decarbonisation programme focused on reducing carbon emissions across north-west England and north Wales. The successful candidate will work closely with Dr. Nicola Scarselli as part
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the Institute’s strategic plan, also have Centre-specific objectives and requirements. The postholder will be based in the Centre for Preventive Neurology. About Queen Mary At Queen Mary University of London, we
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decision-making. They will have strong knowledge of machine learning research methods, excellent programming skills, and the ability to communicate complex ideas clearly to academic and non-specialist
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and methodologies relevant to cryptography research, excellent communication skills, and the ability to present complex information effectively to a range of audiences. They will have strong programming
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machine learning, deep learning, audio/image/video classification, attention mechanisms, zero/few shot learning, and evolutionary algorithms. The Research Associate should have proficient programming skills
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experience in analysing whole genome and methylome sequencing data. Experience in machine learning or AI models. Proficient in Python or R programming. Experience in high-throughput compute cluster and Unix