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Field
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dark-field STEM imaging, energy dispersive X-ray spectroscopy (EDS) and electron energy loss spectroscopy, at the intersection of electron microscopy, software engineering and machine learning. Major
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parts, take acoustic pressure measurements and perform in-situ high speed imaging to study failure mechanisms complemented by microstructural characterisation and analysis. Experiments will be performed
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the mechanosensitive processes and the mechanical properties of tissues that drive tumor malignancy and anti‑tumoral immune responses. This multidisciplinary project will combine: Biophysical techniques and image
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of molecular complexes directly within cells. A central focus of the project will be the development of improved image simulation frameworks. These simulations will not only serve as benchmarking tools
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Intervention invites applications for a two‑year postdoctoral fellowship within the project AI‑Driven Medical Imaging. The position is expected to start on 1 September 2026 or as agreed. The fellowship is funded
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cellular biology approaches. - Analyse tumour samples from pre-clinical models and patient cohorts using high-dimensional platforms, including multiplex imaging, flow and mass cytometry
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As part of the UKRI funded `Glaciers and Ice Sheets in a Warming World' project, we have an exciting opportunity for a postdoctoral researcher who has expertise in image analysis to join the project
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analyses of ant-collected honeydew; and hyperspectral imaging with machine learning to remotely quantify aphid density and physiological state. The results will be field-validated in the context
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William Dunn School of Pathology. The project will involve the mechanistic dissection of protein quality control pathways using quantitative cell-based assays, flow cytometry-based and imaging approaches
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quantitative cell-based assays, flow cytometry-based and imaging approaches complemented by biochemical and, where appropriate, structural methods. The successful candidate will be expected to work independently