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research, fundamental cell biology and cell signalling with excellent shared facilities including the Centre for Proteome Research, the Computational Biology Facility, the Liverpool Centre for Cell Imaging
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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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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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, stable isotope tracers, medical imaging). Experience with molecular biology techniques, stable isotope tracers methodologies, GCMS desirable. -Familiarity with bioinformatics and computational biology
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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 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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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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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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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