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EPSRC PhD Studentship in: Big Data, Network Complexity and Machine Learning to Deliver Targeted Pro-active DWDS Maintenance Department of Civil and Structural Engineering PhD Research Project
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-performance numerical models and data analytics technologies to address some of the key practical challenges in flood modelling and risk management, and finally achieve a web-based platform for large-scale
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orbital mechanics computer programming (in a compiled language such as C, C++, Fortran, or Julia) Experience in the following is advantageous: machine learning and/or artificial intelligence big data
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stable isotope labelling and flux analysis on some of the latest generation equipment. These methods generate large amounts of raw data, and methods to improve effective and efficient data extraction
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-based Tensor Processing Units (TPUs) engine with massive on-chip global buffers for data-stationary. Systolic Array (SA) architectures with novel spatial dataflows will be utilized, at a large scale, for
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to reconstruct statistically meaningful flow fields. Despite their popularity, both approaches still present major challenges such as large amounts of high-resolution data (from direct numerical simulations
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to use large datasets (‘big data’) and the collection of data to be used in economic evaluations. Funding notes The three year studentship covers UK home tuition fees only and a tax-free stipend
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elapsed since birth (i.e. chronological age). However, this simple count cannot explain the large variations in the ageing trajectories that exist between older people of similar age. For these reasons
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analyses. Expertise will be gained in how to use large datasets (‘big data’) and the collection of data to be used in economic evaluations. Funding notes The three year studentship covers UK home tuition
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the project, the analysis should also be probabilistic so that both experimental and modelling uncertainties can be quantified explicitly. Large datasets from real-world lively footbridges excited by walking