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Field
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all, the traditional statistical methods are not suitable for big data due to their certain characteristics: heterogeneity, statistical biases, noise accumulations, spurious correlation, and incidental
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Evaluating the role of Big Data and satellite technologies in understanding livestock impacts on the upland landscape DoS: Dr Mark Whiteside 2nd Supervisor: Dr Katherine Herborn 3rd Supervisor: Dr
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This PhD project will focus on developing, evaluating, and demonstrating advanced data analytics solutions to a big data problem from aerospace or manufacturing system to uncover hidden patens
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statistical methods are not suitable for big data due to their certain characteristics: heterogeneity, statistical biases, noise accumulations, spurious correlation, and incidental endogeneity. Therefore, big
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position is part of a large-scale research project around immune response in neurodegenerative disease. The PhD student will be part of a data science team working on data analysis and experimental design as
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; Midlands Graduate School Doctoral Training Partnership | Birmingham, England | United Kingdom | about 10 hours ago
of the research findings. Person Specification Candidates will be expected to have a first degree in Economics or proximate fields; strong analytical skills; experience in quantitative data analysis using large
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research and / or experience of qualitative research. Expertise will be gained in how to use large datasets (‘big data’) to address clinically relevant questions and how to design and conduct a clinical
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film-forming products such as inks, adhesives, and cosmetics. This PhD position is integrated within a large UKRI funded project on coatings formulations within a world-class research environment. You
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PhD Scholarship - Trophic Interactions in Wood Pasture Ecosystems Department Graduate School Location Ormskirk Salary - Please see "Details of funding" section for information Post Type Full Time
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the predictions of opaque models that are widely deployed in high-stakes decision making scenarios. Of particular interest to this project are example-based explanation methods that use individual data points