10 machine-learning-"https:"-"https:"-"https:" PhD positions at University of East Anglia
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approach that integrates machine learning algorithms, blockchain technology, and IoT devices with digital twin systems. The scientific objectives of the project are as follows: Objective 1: Investigate how
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cell and spectroscopic analysers. Programming (e.g., R, Python) and machine learning for advanced atmospheric time-series analyses. Skills for presenting research at conferences and writing peer-reviewed
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data are needed to enhance our understanding of sources, pathways and impact of litter. Cefas is developing a visible light (VL) deep learning (DL) algorithm and collected a large 89 litter category
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to learn laboratory methods for analysis of relevant BGC parameters. Training: You will be based in the Polar Oceans Team at British Antarctic Survey, a highly active research team focused on both
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spectroscopic methods suitable for large-scale sample screening and eventual field deployment. The project will also involve developing your skills in data science, including multivariate analysis, machine
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measurement; Measurement of related tracers (e.g., Radon); Programming (e.g., R, Python) for advanced atmospheric time-series analyses, including machine learning; Skills for presenting research at scientific
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Interventional Procedures. Research methodology The unified model will leverage 50,000 patient scans from the UK Biobank imaging database, using a semi-supervised learning strategy to learn the orientation
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methods, and associated climate data analyses. You will acquire skills in science communication, project management and collaborative research, and will be involved in a project of critical interest
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some important questions. Did fungi acquire this ability from bacteria by gene transfer, or have fungi evolved mechanisms to degrade DMSP? Is the interaction of fungi with marine plants symbiotic
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, temperature, CO2) conditions from pole-to-pole. This work will be done at UEA. Training The PhD candidate will acquire skills from the bench (e.g., PCR, cloning, phenotyping) to bioinformatics (e.g., Phython