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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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to reconstruct subsurface defects; Implement image/signal‑processing or machine‑learning pipelines for automated flaw characterisation; Collaborate with the Federal University of Rio de Janeiro, including short
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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
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further including to automated platforms to generate large statistical data sets. We will also experiment with untried higher spatial resolution techniques. The large, multi-dimensional data sets will be
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communities bordering the West Nile, Lake Albert, and Lake Victoria. To be considered for the role, you should hold (or be close to completion of) a PhD/DPhil in Health Data Science, along with relevant
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., knowledge representation and reasoning) and bottom-up (e.g., machine learning) methods to study the representation of geographic categories and processes. While we welcome applicants from a broad range of
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for candidates to have the following skills and experience: Essential criteria PhD qualified in mathematical, physical or computational sciences Experience in using machine learning methods to analyse datasets
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expertise in analysing/ training models on biological or chemical datasets Proficiency in Python for data science and machine learning Possess sufficient breadth or depth of specialist knowledge with deep
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, working with some of the world’s largest single-cell 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