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application! We invite applications for a fully funded PhD student position to join the research group of Andrew Winters to work on challenging problems in Computational Mathematics for accurate and reliable
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-temporal machine learning method development, including: generative models for grid-based and particle-based spatio-temporal data; controlled generation methods for data assimilation; and graph-based multi
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image analysis, deep learning as well as mathematics. You have substantial expertise in programming, especially in Python and Matlab. You are independent, meticulous and work efficiently. Since
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methods for data assimilation; and graph-based multi-scale neural network models. While the developed methods will be broadly applicable, particular emphasis will be put on the problem of inferring gas
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part. Your work may also include teaching or other departmental duties, up to a maximum of 20 per cent of full-time. Your qualifications You have graduated at Master’s level in applied mathematics
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infrastructure, and flexibility in delivery both in terms of time and location. You will use mathematical modelling and big data sets to analyze capacity limitations and different charging conditions, and how
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, Electrical Engineering, or Applied Mathematics with a minimum of 240 credits, at least 60 of which must be in advanced courses in Computer Science, Electrical Engineering, or Applied Mathematics. Alternatively