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the PhD candidate may include (non-)linear inverse load estimation and data-driven/machine learning techniques that rely on physics-informed guidance for improved robustness. A key task will be to quantify
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. Alternative approaches are graph-based molecule reaction space sampling and generative machine learning as they provide a path to new synthetic data that can form the basis for a large-scale database of
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are looking for a highly motivated and skilled PhD researcher to work on graph-based machine learning surrogates of wind energy systems. Our goal is to accelerate flexible fatigue load estimation
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(crowding, concentration of nutrients, size of liposome, etc.) with gene expression levels across a synthetic genome. To achieve this aim, you will use VASA-seq and Ribo-Seq to generate large data sets
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to data-driven research projects focused on glaucoma, diabetes or nAMD using large-scale clinical and imaging datasets. Develop, validate, and implement machine learning models and statistical tools
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Supervisors: Dr Raj Pandya, Prof. Nicholas Hine, Prof. Reinhard Maurer While we as humans are used to seconds and hours, electrons and atoms in materials move a whole lot faster around a million
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diagnosis, and knowledge of the operation of helicopter systems. • Confident handling of Python and common data science tools. • Knowledge of high-performance computing and machine learning. • Fluency in
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tools. * Familiarity with tensorflow and/or pytorch. * Demonstrated ability to design and implement machine learning techniques and algorithms. * Demonstrated expertise in the Linux computing environment
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algorithms are used that allow a computer to process large data-sets and learn patterns and behaviours, thus allowing them to respond when the same patterns are seen in new data. This include 'supervised
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PhD Scholarship in Big Data and Analytics for Crop Genetics Modern agriculture and biomedical research are driven by the availability of big data and the development of data science. The recent