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techniques including single cell multiomics, human sample analysis, mouse models of allergic diseases, imaging and advanced flow cytometry. Specifically, the student will characterise memory ILC2 skin niches
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instrumentation Practical experience in biological sample preparation Experience in programming (Matlab, Python) for image and data analysis What you will do Plan and conduct experimental work Develop scientific
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images, clinical data, and genome sequences. Some of these datasets were generated in our lab, while complementary data have been sourced from public repositories. The student will gain a deep
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high-resolution live-cell imaging and structural biology. Examples of methods to be used by the successful applicant are recombinant protein purification, in vitro assays, CRISPR/Cas9 genome engineering
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combination of expertise in computing education research, computerized image analysis, and human-machine interaction (HMI). Within the HMI unit, the Digital Ecologies Lab is led by Prof Mike Hazas and has five
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at understanding the role of neuroinflammation and synaptic loss in Alzheimer’s disease and other proteinopathies with novel PET imaging biomarkers/tracers. Please see the following KI press releases of our recent
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/characteristics for example by documented experiences and through references. Experience in the research area of the PhD or any techniques used in the project including imaging, cell culture, tumour migration
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: Advanced testing on available car- and truck-sized engines. Detailed optical investigations of fuel injection, mixing, and combustion processes using high-speed video imaging and laser-spectroscopic
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. You will also work with models that can include many types of information, from species’ relationships to climate conditions, to provide a clearer picture of how ecosystems evolve and respond to change