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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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, the supervision team have obtained data access to indoor environment sensor data at national scale from a leading industrial collaborator. To pair with this big dataset, outdoor environment data at MetOffice can be
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datasets, therefore, there will be a focus in the implementation of models for large volumes of data. The project will work in an exciting interface of statistics and machine learning and has the potential
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This is an exciting opportunity to explore the role of complex microbial communities in promoting resilience and maximising yields in large scale algal bioreactors exposed to different environmental
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problems in health data science. Air pollution is composed of several different environmental pollutants, for example particulate matter (PM10 and PM2.5), ozone (O3), nitrogen dioxide (NO2) and sulphur
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the analysis of the complex data and cellular models (Big Data and Kavli Institutes). The DPhil will provide the student with multidisciplinary skills including specialized training in bioinformatics, genetic
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. The student will perform ‘big data’ analysis of patient cohorts including time-based evaluation of the impact of introducing CT-FFR as a national health intervention into a healthcare system. Exploratory
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Fully funded positions available to start in 2025 or early 2026 Projects span plant genetics, climate-resilient breeding, AI and data science, soil health, sustainable agriculture, circular economy
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Human Behaviour in projects targeting social good. Research at N/LAB focuses on the development and application of innovative computational methods using Big Data, Behavioural Science and Machine Learning