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Deadline: All year round How to apply: uom.link/pgr-apply-2425 How many positions: 1 Open to: UK students This 3.5-year PhD project is fully funded by The University of Manchester and the funding is
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, or physics, and will demonstrate a willingness for experimental fieldwork and data analysis. This PhD offers a unique opportunity to contribute to frontier research in atmospheric chemistry, with
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Qualification Type: PhD Location: Nottingham Funding For: UK Students Funding amount: Full tuition fee waiver pa (Home Students only) and stipend at above UKRI rates pa (currently at £20,780
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effective delivery of expertise, equipment, and medical resources in response to complex and large-scale emergencies across the United Kingdom. In its initial phase, the research will examine past and
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To apply for this PhD opportunity please complete the application form using the 'Apply' button above. For further information please contact: Name: Mr Angelos Plastropoulos Email: a.plastropoulos
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace - In Partnership with Rolls-Royce PhD
Overview: Cranfield University invites applications for a fully funded 3-year PhD, supported by the EPSRC DTP and Rolls-Royce. This studentship covers tuition, a tax-free stipend, funding
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; EPSRC Centre for Doctoral Training in Green Industrial Futures | Bath, England | United Kingdom | 3 months ago
This is a fully-funded 4 year PhD offering an annual tax-free stipend of £20,780, tuition fees and an enhanced research and training grant. This PhD is one of a number of projects hosted by
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contemporary high-resolution next-generation sequencing and array-based genomic and epigenomic datasets across large cohorts of human tumours and experimental models, alongside complex drug screening, efficacy
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analyse large datasets such as the Clinical Practice Research Datalink (CPRD) and Hospital Episode Statistics to identify activity related to the treatment of community acquired pneumonia. This will require
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of innovative computational methods using Big Data, Behavioural Science and Machine Learning to understand behaviour through the lens of digital footprint/“smart data” datasets, cutting across sectors ranging