634 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "Edinburgh Napier University" scholarships in United Kingdom
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PhD Studentship: Bottom-up Decoding of Protein Conformational Landscapes: from Gas-phase to Solution
folding simulations with mass spectrometry experimental data, creating a unique multi-stranded methodology to map out free energy landscapes associated with protein folding in environments spanning gas
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· leave the ‘Research Area’ field blank · select ‘PhD Energy Materials' as the programme of study You will then need to provide the following information in the ‘Further Details’ section: · a
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Area’ field blank Select ‘PhD Energy Materials' as the programme of study You will then need to provide the following information in the ‘Further Details’ section: A ‘Personal Statement’ (this is a
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may be required to obtain UK Security Clearance. Funding This studentship is open to UK applicants only. How to apply For further information please contact: Name: Prof. David MacManus Email
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Decision support systems assist decision makers in instances when information available for decision making is vast and beyond human information processing capabilities. The Video Assistant Referee
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in October 2026. Estimating causal effects of psychiatric treatments from observational data is undermined by confounding variables that are rarely captured in structured records, but are richly
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. Supervisors:Professor Paul Sellin , Professor Carol Crean and Dr Ian Riddlestone Entry requirements Open to candidates who pay UK/home rate fees. See UKCISA for further information . Starting in October 2026. You will
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of light. [1][2] This emerging technology holds enormous potential, offering routes towards new forms of highly parallelised information processing (optical computing), point-of-care diagnostics, next
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, self-starter, PhD student to run the follow up questionnaires and analyse the data. This offers an exceptional research opportunity to investigate the contribution of lifestyle factors, particularly
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data-driven methods to develop an inverse design framework for manufacturing systems. Together, we will advance the capability to design manufacturing systems that embed reliability, resilience