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implemented to allow the prediction of these mesoscale properties. The AI model's predictions will serve two primary purposes: establishing further correlations with relevant bulk properties of actual materials
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Subject area: Arts and Humanities, Science, Social and Health Services Closing date of advert: 16th June 2024 Start date: October 2024 Name of primary supervisor/CDT lead: Emma Pullen Name(s) of
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Closing date of advert: 16th June 2024 Start date: October 2024 Name of primary supervisor/CDT lead: Dr Emma O'Donnell Name(s) of secondary supervisor(s): Dr Richard Ferguson, Dr Laura Barrett
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subject and preferably a relevant masters qualification and/ or extended laboratory research experience (e.g. molecular biology, biochemistry, cell culture, confocal microscopy). A strong background in
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Civil Engineering or Computational Physics. They should demonstrate aptitude for original research. The candidate should possess a good understanding of construction processes and data processing
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degree (or equivalent) in biosciences including Biochemistry, Biomedical Sciences, Cancer Science or related discipline. A Masters degree in a relevant subject and/or experience in laboratory-based
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which is equivalent to at least 25% of a year’s full-time study in the fourth year, with achievement of a grade for the project equivalent to a H1 standard or 80% OR A Masters degree, with a significant
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Second Class honours, and a master's degree. Alternatively, applicants with a UK first class honours degree (or Non-UK equivalent as defined by Swansea University) not holding a master’s degree, will be
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equivalent to UK honours (by UK ECCTIS) and achieved a grade equivalent to UK Upper Second Class honours, and a master's degree. Alternatively, applicants with a UK first class honours degree (or Non-UK
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theory, the primary objective is to analyse the multifaceted interactions among direct/indirect stakeholders, seeking to uncover how they collaborate and interact to create and foster transformative value