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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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a series of high-resolution FRET biosensors to track such dynamics for gibberellin, abscisic acid, auxin and salicylic acid in living plants. The Jones group combines imaging of FRET biosensors with
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The Role The successful applicant will be responsible for the design, development, and implementation of deep learning and computer vision frameworks across a range of research projects
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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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We wish to appoint an exceptional person to join the Magnetic Resonance Imaging (MRI) team within the Neurological Disorder and Imaging Section to provide a novel and proactive approach to data
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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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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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Team Leadership skills Desirable: D1 Acquisition and analysis of FLIM-FRET dataD D2 Image processing skills relevant to motion artefact correction D3 Computer interfacing of electronic devices Experience
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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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prototype Total Factor Productivity (TFP) measure that includes non-market agricultural outputs and impacts beyond environmental indicators. The ideal candidate will hold a PhD in agricultural economics