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Field
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investigating adaptive neural circuits underlying distinct forms of behavioural flexibility. The postholder will be responsible for designing, developing, and operating advanced large-field or multi-brain region
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multimodal data (including MRI, MEG, EEG, and genomic data). The postholder will work with a team with a strong track record in Big Data analytics in child mental health, helping accelerate the move toward
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, funded by the UKRI Frontier research grant titled “StochFields”. They will be expected to conduct research which falls within the remit of this large-scale project and will have the opportunity to do so in
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, funded by the Simons Foundation grant titled ‘Simons Collaboration on Probabilistic Paths to Quantum Field Theory’. They will be expected to conduct research which falls within the remit of this large
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, and computational humanities. The postholder will lead the curated stream of the project, which involves designing a large corpus of Latin texts, curating it (correction of pre-processed data and corpus
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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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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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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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programming and scripting (e.g. R, Python, Bash) for data processing, integration and visualisation Proven experience developing and using necessary pipelines for analysis of large-scale bulk and single cell
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connected large wind energy system dynamic modelling, control and analysis. In particular, the objective of this research programme is to lay the foundations of a new, model and methodology for Advanced wind