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experience of the laboratory delivery of multiplex imaging datasets of tissue samples as well as significant laboratory experience of general methods including immunoassays and cell culture. You will have
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responsibility for implementing a deep learning work-package as part of a Cancer Research UK-funded programme, developing an image-recognition model to identify morphological features corresponding to clonal
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to target specific transcription factors (iii) use of high content imaging and AI to phenotype these cultures (iii) use of bulk and single-cell RNAseq to characterise the transcriptional profile of each cell
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techniques, multispectral flow cytometry, and bioinformatic analysis of transcriptomic (RNAseq) datasets. Experience with image analysis, particularly for immunofluorescence, is desirable. You should be
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models of spillover infection and transmission of prototype viruses representing viral families concern to support the development of methods for virus sequence analysis and inference of human transmission
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the Medical Research Council and Medical and Life Sciences Translational Fund. It offers an exciting opportunity to work in a vibrant multi-disciplinary team that crosses the domains of clinical imaging and
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Professor Hing Leung and the wider multi-disciplinary research team. We are looking for candidates with experience/strong interests in learning some of the following: deep learning, medical imaging, and
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with strong expertise in molecular, cellular, biochemical biology and imaging. Candidates should be highly motivated individuals with excellent communication skills and have the ability to work
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of mutants by barcode sequencing, flow cytometry and imaging. 3. Develop and establish tailored single-cell RNAseq protocols to study mutant parasite populations. 4. Develop and enhance your research profile
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new paradigm of deployable nanoelectronic devices with unprecedented efficiency and scalability that can be used ubiquitously towards ultra-low energy QC, AI, and neuromorphic computing systems