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Field
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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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ability to evaluate fossil fuel CO2 (ffCO2) emissions is currently limited. ‘Bottom-up’ emissions estimates, based on inventory-style accounting and mobile tracking data, can differ significantly from each
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experimental chemistry. Additional training in a wide range of soft and hard skills is available at the University. What you will do: This PhD project aims to bring together multi-scale modelling and data-driven
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Please note, this is a PhD Scholarship co-funded by Foundations, the National What Works Centre for Children and Families and the biggest funder of evaluation research in early interventions
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. You will focus on machine learning, but will be involved in all areas. There are also spinout opportunities. For details: PhD information sheet The team have wide experience studying bumblebee behaviour
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visiting SRC for collaborative data processing and participating in seismic survey on the island arc. The candidate will benefit from working with a large multi-disciplinary research team recently funded by
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for appointment at Grade 7 with a salary range of £39,424- £47,779 per annum with amended duties and responsibilities. About us The MMM Unit is based at the Big Data Institute and John Radcliffe Hospital. We work
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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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specific courses aimed at the different phases of a PhD. For example, the programme includes aspects such as research methods, technical report writing, presentation skills, data management, leadership