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, preferably MRI. Experience with tumor segmentation, radiomics, or deep learning–based image processing is expected. Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow is required
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discovered mechanism by which neurons remove dangerous “trash” in a project relevant to human neurodegenerative disease mechanisms. The experienced PhD scientist hired will work on a focused project on the
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random but can only happen along particular directions. However, as the material is made up of many crystals, and they all have different orientations, the deformation process of a polycrystalline material
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scaled-up, optically clear model of the new dialyser. You will apply advanced techniques, specifically Particle Image Velocimetry (PIV) to resolve complex fluid flow behaviour. This role offers a unique
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of experimental data for the development of the new theory. Such methods include (but not limited to) optical tomography for 3D imaging of the polymer crystallisation process, high-resolution, real-time Atomic
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scientist with proven intellectual and technical abilities to work on a synthetic organic chemistry project. The project, funded by the Leverhulme Trust, aims to develop a new paradigm in organic synthesis
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will include electron microscopy, expansion microscopy, in situ fluorescence labelling of neuronal cell types using RNA and antibody probes, image segmentation and neuronal tracing, and network analysis
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& Sciences (SCMMS) provides an outstanding multi-disciplinary environment for the pursuit of cutting-edge cardiovascular and metabolic research (https://www.kcl.ac.uk/scms ). We study the fundamental molecular
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Aleks Tata lab in the Department of Surgery, Duke University School of Medicine has an opening for postdoctoral researchers to study mechanisms in lung injury-repair. We seek to understand the cellular
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longitudinal consistency and data quality, including parameter tracking, protocol versioning, and rigorous QC frameworks Implementing, validating, and extending image-processing and analysis pipelines