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group aims to determine regulatory pathways affected by disease by implementing the use of spatial proteomics combined with transcriptomics and live imaging. The total proteome of a neuron includes a vast
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-time systems. The role will involve working with large and multi-modal datasets (e.g., images, video, audio, and sensor data), and deploying solutions in real-world environments, particularly in robotics
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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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Taylor and a wider team that includes Glasgow and Edinburgh University life scientists. Specifically, the job requires expert knowledge in optical microscopy, including FLIM-FRET imaging and advanced image
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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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to convert CO2 into valuable bioproducts, advancing sustainable bioeconomy. It heralds a paradigm shift on sustainable production of chemical compounds, food and materials to displace fossil fuels
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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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immunofluorescence-based imaging techniques: light sheet microscopy, clearing and whole mount imaging, multicolour immunofluorescence microscopy, IBEX, expansion microscopy, or similar. Previous experience in
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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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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