397 structural-engineering-"https:"-"https:"-"https:"-"https:"-"https:"-"UCL" PhD positions in United Kingdom
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of Engineering. The BEE Research Group conducts cutting-edge research into low-energy buildings, building performance, retrofit and decarbonisation, indoor environmental quality, and climate-resilient design
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/postgraduateresearchphdopportunities/engineering/chemicalprocessengineering/laserimagingofsustainablenanomaterialsforadvancedenergyapplications/#apply You will be required to upload your CV, academic qualifications and two references
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Manufacturing Technology Research Group at Faculty of Engineering which conducts cutting edge research into sustainable high-value manufacturing processes. Vision We are looking for a PhD student who is motivated
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years EligibilityUK, EU, Rest of world Reference numberSATM450 About the host University and Through-life Engineering Services (TES) Centre Cranfield is an exclusively postgraduate university that is a
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This exciting opportunity is based within the Building, Energy and Environment Research Group at Faculty of Engineering, which conducts cutting edge research into the sustainability of buildings and
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Towards a Flower Waste Biorefinery: Predictive Enzyme Design for Bio-Based Chemicals This exciting opportunity is based within the Faculty of Engineering, which conducts cutting-edge research in
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) Research Group and the Department of Mechanical, Materials and Manufacturing Engineering (M3) and within the Faculty of Engineering at the University of Nottingham. The PEMC is a global leader in power
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evaluation. Prognostics is an essential part of condition-based maintenance (CBM), described as predicting the remaining useful life (RUL) of a system. It is also a key technology for an integrated vehicle
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with Graphs led by Prof. Nils M. Kriege. Our research focuses on the development of new methods and learning algorithms for structured data. Graphs and networks are ubiquitous in various domains from
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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