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Field
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effective strategies for preventing, identifying and stopping the abuse of older people? While the advanced methods training of the PhD will be data science and statistical analysis, there will also be
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using a range of geoelectrical techniques, GHG soil flux measurements and associated soil-based analyses, as well as analysis of ecosystem GHG fluxes and remotely sensed data. We will support the PhD
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background in physics, biophysics, biological physics, or bioengineering. This PhD project will primarily focus on experimental research, which will include data analysis and there is scope for modelling
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approaches, including principal component analysis and machine learning, to handle multivariate datasets. Prior experience in data science is not essential; the successful candidate will be supported through
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, and space hardware. This PhD research aims to develop a comprehensive Mode Selection Framework for Reduced Order Modelling (ROM) in Structural Dynamics—using machine learning to build robust
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been collected in Malawi under a joint agreement with the Government of Malawi, with field phenotype data, genomic sequencing data obtained in the UK, and micro-CT scanning data being also collected
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myriad of new techniques and much sought-after skills demanded by industry, including the following: Design of experiments, data harvesting/analysis and interpretation of results to derive insight
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for household who stay indoors, and to prepare for emergency responses. Possible quantitative methodologies include concurrent time-series analysis of outdoor and indoor environment data, prediction model
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define observable events based on expert knowledge and available evidence. Development of a post-race analysis structure, process and data ‘toolkit’ that can build on historical understanding of race
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-based reviews, data collection and analysis, written outputs, and the dissemination of research findings to different audiences, including through investor briefings and academic publications. We