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structures has become a vital strategy for minimising the environmental and economic costs associated with demolition and new construction. However, one of the most pressing threats to structural integrity is
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into meaningful information. This data includes measurements, images, text, recipes, and more. The project addresses the real-world challenge of extracting value from semi-structured textual data, a
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on the design, fabrication, and characterisation of MEMS devices. The first project will focus on a new type of gas flow sensor that is based on the deformation of integrated mechanical structures due
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performance simulation capabilities for gas turbine engines developed at Cranfield University as the starting point. Applications are invited for a PhD studentship in the Centre for Propulsion and Thermal Power
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We are Austria’s largest research and technology organisation and an international player in applied research for innovative infrastructure solutions. This makes us a powerful development partner
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approaches to thermal management in devices. The central science questions are: (i) how acoustic standing waves govern the organisation of 2Dm in liquids/solids, (ii) how structural order influences (optoe
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to 1) Object-attribute compositionality to replace exhaustive data requirements with structured concept learning, 2) Bias detection and machine unlearning to identify and mitigate bias and shortcuts
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Join an exciting research journey at the intersection of advanced materials, electronics, and textile engineering. This PhD project will explore smart structural design, manufacturing and
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temporal structure and missing data affect multimodal learning Identifying fundamental trade-offs between accuracy, efficiency, and robustness. The project emphasises foundational machine learning research
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of flexible trailing edge with both hydrodynamics and acoustics. The simulation results will be analyzed and compared with analytical models, and then inform a refined low-order taking flow-structure-acoustics