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applicant should have prior experience in data analysis, data visualization, and preferably the development of interactive web-applications through approaches such as Bokeh, Shiny, Taipy, Streamlit etc
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analysis. The engineering targets identified using computational approaches by the successful applicant will be validated in crop plants by other members of the consortium. This is an exciting collaborative
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new data will be linked with information from samples previously collected during pregnancy, there will also be opportunities to be involved with analysis of multi-omic data to study mechanisms
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biological science, together with relevant laboratory and computational experience (flow cytometry, cell culture, molecular sub-cloning and analysis of next generation sequence data). Experience in coding and
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computer programs to design experimental paradigms, analyse data and conduct advanced statistical analysis. Prior experience in running neuromodulation studies including TMS and TUS is essential. You will be
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, fluorescent microscopy, etc.), as well as extensive experience in quantitative proteomics (both sample preparation and data analysis) is expected. As a postdoctoral researcher, you are expected be able
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. The project will also involve close collaboration with researchers from other disciplines within the Oxford Martin Circular Battery Economies Programme, who are working on second-life battery analysis, re
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, or similar languages, and proficiency in high-performance computing. You will have experience in large-scale genomic data analysis. You will be able to demonstrate how to organise and prioritise work
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ZEISS LLSM, ZEISS Elyra, ZEISS 980 confocal, PicoQuant FLIM, PicoQuant Luminosa, and bioimage analysis of fluorescence microscopy images. You must have the ability to manage your own academic research and
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scheduling in compliance with ethical standards and data protection regulations. You will also perform pre-processing and basic analysis of neuroimaging data under the guidance of senior researchers and