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. This project will focus on learning about patient experiences and exploring clinical data to improve the care of patients living with kidney disease. What is involved The aim of this research is to improve
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) cluster , which the student will use for their research. The student will also have access to large HPC systems run by DIRAC . The student will be trained in using HPC by the supervision team and at
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. when do we stop modelling? How do we track / score the quality of the model What is the required level of quality over time How can quality be brought to the required level Can Machine Learning, Large
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(Southampton and Denmark), and participation in large-scale testing in Denmark to validate industrial applications of your research. This project is an excellent opportunity for candidates who are interested in
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research and large and thriving graduate research community. We provide an active and stimulating environment to carry out your doctoral research and have an extensive range of specialist labs and facilities
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-to-apply/apply-online.aspx For further information please email Professor Chris Gerada , Dr. Tianjie Zou (University of Nottingham) and Dan Walton (MTC). Facilities The MTC is an independent Research and
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the ‘Apply’ button above. For further information please email Professor Chris Gerada , Dr. Tianjie Zou (University of Nottingham) and Dan Walton (MTC). Facilities The MTC is an independent Research and
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V236E (ε3), which reduce the risk of AD by 2–3 times. This project will use bioinformatics and big data and induced pluripotent stem cell (iPSC) models —cells generated in the lab that can mimic brain
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for example synchrotron based experiments. More information on these facilities can be found; There will be many rewards: Working as part of a large and experienced team across the cohorts will ensure a
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of the research findings. Person Specification Candidates will be expected to have a first degree in Economics or proximate fields; strong analytical skills; experience in quantitative data analysis using large