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(or thesis submitted and awaiting examination) in Psychology, Psychiatry, Epidemiology, Statistics, Health Data Science, Public Health, Criminology, or a related discipline. Experience analysing large-scale
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males and females respond differently to bacterial infection. The successful candidate will play a leading role in the imaging and data analysis components of the project. They will develop and apply high
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. Experience analysing large biological datasets, including RNA sequencing, metabolomics, proteomics or whole-genome sequencing data, is essential, along with strong quantitative and computational skills
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research nurses in a collaborative research environment. For further information, please contact Responsibilities will include assembling, managing and analysing large real-world datasets to address defined
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, bioelectrical signalling and fibrotic cell-state transition. More information on the project can be found here: https://marcfernandezyague.com The successful candidate will conduct experiments at the Queen Mary
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demonstrate equivalent research experience and qualifications. Experience in both wet-lab and dry-lab research is beneficial. You must have expertise in analysis of large-scale genomic data and confidence in R
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assistive technologies and contributing to high-quality publications in leading HCI venues such as ACM CHI and ASSETS. The researcher will have the opportunity to be involved in a large number of exciting
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translocations between proto-oncogenes and immunoglobulin genes are the defining lesions of mature B cell lymphomas including diffuse large B cell lymphoma and Burkitt lymphoma. They arise as a byproduct of
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studies (GWAS), fine-mapping, colocalisation, polygenic risk scoring, and Mendelian Randomisation; and (ii) deep phenotyping of multi-modal cardiovascular imaging (MRI, CT, echocardiography) from large
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studies (GWAS), fine-mapping, colocalisation, polygenic risk scoring, and Mendelian Randomisation; and (ii) deep phenotyping of multi-modal cardiovascular imaging (MRI, CT, echocardiography) from large