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experts to acquire bespoke training and testing data; develop prototype solutions informed by the latest ideas in medical imaging AI, computer vision and robotic guidance; and evaluate models in simulated
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We are seeking a full-time Postdoctoral Research Assistant to join a cross disciplinary research project to improve our understanding of colorectal cancer. Deep learning has revolutionised image
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with cutting-edge models and technologies—including patient-derived glioblastoma organoids, CRISPR-based screens, mass cytometry, and advanced microscopy—to dissect these complex biological processes
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-on experience in at least one of single-cell or spatial omics, imaging, or other high-dimensional biological data types. You interrogate existing literature critically, design rigorous experiments and deliver
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experimental and computational approaches are employed to shine light into key biological processes during the life of parasitic flatworms. Large-scale sequencing datasets (‘omics’) are generated and analyzed
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migration, nanoscale assembly, or complex charge-screening processes are still poorly understood despite their critical impact on electronic properties and device performance. The project will provide a
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biological, and live cell fluorescence imaging experiments. Associated structural analysis of the proteins by cryo-electron microscopy will be undertaken via collaboration with other workers. This full-time
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collaboration with the Translational Gastroenterology Unit (TGU) and the Ludwig Institute of Cancer Research (LICR) we aim to develop a computer guided endoscopy image recognition system that will support
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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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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