46 multiple-sequence-alignment Postdoctoral positions at University of Oxford in United Kingdom
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transmission at multiple scales’, working with researchers at the Universities of Manchester and Cambridge, and the London School of Hygiene and Tropical Medicine. The project will involve the analysis of whole
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research aligned with the project aims outlined by Dr Pathania and funded by Ludwig Cancer Research. You will critically analyse, interpret and present experimental data with scientific rigor and
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to take the next step in your career to become a world-leader in hyperpolarised imaging. The Xenon and 13C teams share staff which enables cross-disciplinary idea sharing and sequence development, as
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and decision-making in humans and machine learning systems. The post-holder will have responsibility for carrying out rigorous and impactful research into human-AI interaction and alignment, with a
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, including RNA work, protein purification, mammalian cell culture, next generation sequencing genome-wide analyses (ChIP) and mass spectrometry. You will establish and optimise protocols, design and accurately
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use computational approaches to mine natural biodiversity in gene sequences to identify engineering targets to increase lipid content and enhance the water use efficiency. The project will make use
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techniques for example multi-parameter flow cytometry, cell culture, systems serology, ELISpot and ELISA. You should have some knowledge of single-cell RNA sequencing (RNAseq) and other relevant next
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for example multi-parameter flow cytometry, cell culture, systems serology, ELISpot and ELISA. You should have some knowledge of single-cell RNA sequencing (RNAseq) and other relevant next generation sequencing
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sequencing, spatial and single-cell multi-omics. This research could deepen the fundamental knowledge and promote translational/preclinical development. You must have a proactive and adaptable approach to work
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longitudinally collected sequencing datasets (both Illumina and Oxford Nanopore) of clinical isolates that can be linked to electronic healthcare record data and/or metagenomic data. These unique datasets provide