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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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) a PhD in a quantitative discipline such as computer science, mathematics, statistics, engineering, or a related field. Strong programming skills and experience in machine learning or statistical
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, data normalisation and machine learning methods applied to biological datasets Experience with data management and version control (Git/GitHub, workflow automation, documentation) Capacity to work
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skills. Aware of the ethical issues around working with Big Data. Desirable criteria Experience applying advanced statistical or machine learning methods to complex datasets. Evidence of involvement in
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lead analyses of large-scale datasets, applying advanced computational and statistical methods to integrate multimodal data (including MRI, MEG, EEG, and genomic data). The postholder will work with a
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Samuel Kaski’s research group Probabilistic Machine Learning is searching for postdocs to work on AI fundamentals in exciting projects. The work includes collaboration with ELLIS Institute Finland
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lead analyses of large-scale datasets, applying advanced computational and statistical methods to integrate multimodal data (including MRI, MEG, EEG, and genomic data). The postholder will work with a
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data assets, and in the context of the world’s largest longitudinal population studies, many hosted here at the Big Data Institute, as well as other international initiatives. To be considered, you must
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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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skills (e.g. Python, Julia) to merge concepts of chemical engineering, operations research and computer science, as you may also need to deploy machine learning to support data analytics and complex