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into the learning mechanism. The integrating should enable to guarantee certain properties of the learned functions, while keep leveraging the strength of the data-driven modelling. Most of, if not all
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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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internal data but often disrupt the process, alter the thermal fields, and risk damaging the part during fabrication. Finite element analysis (FEA) models, while capable of delivering detailed
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mathematics, data science, computer science, food science, microbiology or related fields. It is especially well suited to individuals who are curious about how thermal processes, heat and mass transfer
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We invite applications from ambitious life-science graduates for a fully funded PhD at Cranfield University, supported by the EPSRC Doctoral Landscape Award. The award covers Home fees (£5,238 p.a
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based at Cranfield University, which has extensive expertise in soil science, environmental data and predictive soil mapping and curates the LandIS national soil information resource. The studentship
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, microbiology, environmental science, ecology, materials science, mechanical engineering or related disciplines. An interest in experimental research and quantitative data analysis would be beneficial
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materials science, coating deposition process technology, and device engineering for non-flat optics. The main objectives and tasks are as follows: Materials Engineering: Critically review literature
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Engineering, Ocean Engineering, Offshore Engineering, Renewable Energy, Structural Engineering, Electrical Engineering, Computer Science, Artificial Intelligence, Data Science, or other relevant STEM
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, Artificial Intelligence, Data Science, or other relevant STEM disciplines. Experience in one of more of offshore systems, sustainability assessment, programming, data analysis or AI modelling would be