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hyperparameter optimization and conformal prediction methods; The online monitoring of model reliability and performance indicators in dynamically changing environments; The integration of synthetic data from
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should have experience analysing data in large developmental cohort studies, experience conducting meta-analysis, experience with using structural equation modelling and working with statistical software
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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
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We invite applications for a Postdoctoral Researcher to join the research group of Professor Christopher Yau ( http://cwcyau.github.io ) at the Big Data Institute, University of Oxford. This post
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relevant for hadron collider synchrotrons at the high-energy frontier, such as the Large Hadron Collider (LHC) and its High Luminosity upgrade (HL-LHC) at CERN, the European Laboratory for Particle Physics
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PhD in Computational Biology, Bioinformatics, Computer Science, Statistics, or a related quantitative field * Demonstrated expertise in programming and scripting (e.g. R, Python, Bash) for data
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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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develop the foundational and scalable tools, technology and methods needed to synthesise large sections of human genomes/chromosomes. Through programmable synthesis of genetic material the aim is to unlock