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Field
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Supervisors: Prof Cathy Merry, Prof. Kenton Arkill, Dr Andrew Hook Overview Glycosaminoglycans (GAGs) are linear sugars that are displayed on all cells throughout the body as well as in the matrix
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for greater precision. Machine learning (ML) algorithms will analyse these datasets to deliver a scalable, cost-effective system, validated through field trials and enhanced by contributions from four
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ability to work independently and collaboratively. To apply, please contact the supervisors Dr Bissett - mark.bissett@manchester.ac.uk and Prof Kinloch - ian.kinloch@manchester.ac.uk . Please include
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context. The work will include, but is not limited to: investigating new mathematical formulations of the underlying physics; developing fast algorithms and numerical methods that leverage modern parallel
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Early Years (www.isey.org ) in London, UK. Supervision will be by Prof Sam Wass (www.profsamwass.com ) (UEL, UK), with Prof Davidson (UEL, UK), Prof Rachel Barr (Georgetown, US) and Dr Sarah Jessen
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be by Prof Sam Wass (www.profsamwass.com ) (UEL, UK), with Prof Davidson (UEL, UK), Prof Rachel Barr (Georgetown, US) and Dr Sarah Jessen (Lübeck, Germany) as co-supervisors. The PhDs will start in
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and social care research (Prof. Anderson), applied statistics and analysis of large routine datasets (Prof. Abel), and substantive knowledge of the abuse of older people and related policy issues and
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Supervisory Team: Prof Middleton, Prof Gandhi PhD Supervisor: Matt Middleton Project description: We know of only 20 or so black holes in our galaxy yet predict there should be 10s of millions
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4-Year PhD Studentship: Deciphering how domain organisation regulates heparan sulphate function Supervisors: Prof Cathy Merry, Prof. Kenton Arkill, Dr Andrew Hook Overview Glycosaminoglycans (GAGs
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). Supervisors Primary supervisor: Prof. G.Tasca. Co-supervisors: Prof. J.Diaz-Manera, Dr. S.Cockell. Eligibility Criteria You must have, or expect to achieve, at least a 2:1 Honours degree or international