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technologies, bulk and single-cell RNA-sequencing, flow cytometry, multiplex immunofluorescence, and standard molecular biology and biochemistry techniques. A computational component may also be available
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This PhD project will focus on developing, evaluating, and demonstrating advanced data analytics solutions to a big data problem from aerospace or manufacturing system to uncover hidden patens
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at jun.jiang@imperial.ac.uk Further information on research in mechanical engineering at Imperial College London can be found at: https://www.imperial.ac.uk/mechanical-engineering/research/ and details
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of mathematics, statistics, data science and physics to make real impact outside of academia and to interdicsiplinary research. NU Solve also operates a drop-in clinic for University staff to get help with
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need to contact the project supervisor to discuss. Online applications are made via the 'Apply' button above. Please select Engineering Mathematics* on the Programme Choice page. You will be prompted
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computational and machine learning approaches to integrate Oxford Nanopore (ONT) long-read data with bulk and single-cell RNA-seq profiles. The aim is to identify host-microbiome molecular signatures that drive
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will be jointly supervised by: Dr Dominik Leichtle, School of Informatics, University of Edinburgh Dr Elham Kashefi, School of Informatics, University of Edinburgh Dr Theodoros Kapourniotis, National
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computational methods to optimise the quality of doubly curved shell structures manufactured from recycled, short-fibre composites. A particular novelty of the research will be the inclusion stochastic elements
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The University of Exeter’s Department of Computer Science is inviting applications for a PhD studentship funded by Faculty of Environmental Science. For eligible students, the studentship will cover
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of medulloblastoma biology, focussing specifically on tumour metabolic targets. You will develop and support the INSTINCT-MB. programme through the provision of experimental, analytical and bioinformatic expertise