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involves working closely with team members with different research expertise, and also supervising current PhD students in the group. About You Candidates should have a PhD in mechanical engineering
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are seeking highly-motivated researchers with a submitted PhD or PhD in molecular biology, genomics, immunology, bioinformatics or a relevant discipline with strong technical expertise in multi-omics approaches
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. This position is part of the ERC consolidator grant of Dr Thomas Haworth (funded by UKRI guarantee funding). The team will maintain around 3 postdocs and 2 PhD students, with flexibility to work together and with
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distant candidates for resolved observations with the ELT. This position is part of the Royal Society Dorothy Hodgkin Fellowship of Dr Thomas Haworth. There will be a PhD student on this grant too that
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imaging, electrocardiogram (ECG) and electronic health record (EHR) data to improve diagnosis, management and outcomes of heart failure. About You You will have either a PhD in the relevant subject (e.g. in
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plan and conduct experiments, generate high-quality data, prepare publications, make presentations and help supervise associated PhD students. The successful candidates will join large, supportive
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global primary care and public health. About You You will have PhD or equivalent experience in bioinformatics, statistics, epidemiology, or a relevant field such as computer science, operational research
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student facing and are ideally suited for people who have recently completed a PhD and who are interested in developing their educational skills within a supportive department. They should be enthusiastic
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the joint supervision of Dr Gregory Chass and Dr Devis Di Tommaso in the School of Physical and Chemical Sciences at Queen Mary University of London (UK). About You The ideal candidate should possess a PhD
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. Applicants at the PDRA level must have a PhD in Computer Science or a related field. Expertise in intelligent decision-making under uncertainty is essential, relevant areas include influence diagrams, causal