586 systems-science-"https:"-"https:"-"https:"-"https:"-"BioData"-"BioData"-"BioData" PhD positions in United Kingdom
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PhD Studentship: SURVAI – Smart Urban Representation for Visual AI Infrastructure Management The project How can we build AI systems that understand entire cities, from satellite height to street
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to close-to-magnet particle doses. The stability of a given austenite grain is governed by local microstructural parameters such as the local chemistry, grain size, orientation, the surrounding microstructure
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carried out under the UK Hypersonics Doctoral Network. This network is supported by the Ministry of Defence and EPSRC for building the necessary expertise to develop next-generation hypersonic vehicles
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is critical to enhancing the economics of large-scale offshore wind energy projects. Prediction of the localised turbulent wind conditions onset to each turbine location is a key input to determine
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Studentship Information Supervisor: Vinay Shukla Subject Area: Plant & Crop Science Research Title: Root oxygen dynamics and development Research Description: The student will be part of a
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. How to Apply: All applications should be made online . Under programme name, select Electronic, Systems and Electrical Engineering. Please quote the advertised reference number: FP-SA26-CP in your
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to older adults, with some studies showing non-financial challenges to be the greater issue. Better understanding of food provisioning among older people is a crucial priority area for the project partner
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an efficient and mature technology, yet it requires high temperatures and has a large carbon footprint. This PhD project addresses a key challenge: efficiently producing bio-methanol from abundant
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degree/ background in Business Analytics/Data Science with some experience of dealing with Natural Language Processing (NLP) and Machine Learning (ML). It is not necessary for the candidate to have prior
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap