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project to develop a systematic framework for reconstructing the evolutionary histories of pathogens. The role involves using viral sequence data and models of sequence evolution to investigate both
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helminths, particularly hookworms. This project will measure anthelminthic resistance and then genome sequence worms to investigate the genetics of anthelminthic resistance. This role will be focussed
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project “Fern seed found? Testing a new hypothesis for the origin of seed gene networks”. This project aims to use reverse genetics, cross-species complementation and single cell next-generation sequencing
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with sequencing bacteria in the laboratory. Key attributes of the successful applicant include: Experience with REDCap: setting up databases Experience of working in a healthcare setting, medical research
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Python/R and Linux/UNIX, or expertise in structural variant analysis with motivation to upskill computationally. Experience in cancer genomics/epigenomics and high-throughput sequencing. Proven track
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, the development and fine-tuning of vision foundation models, multiple instance learning, survival analysis, and interpretable model development. You will also lead efforts in building multimodal deep learning
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, the development and fine-tuning of vision foundation models, multiple instance learning, survival analysis, and interpretable model development. You will also lead efforts in building multimodal deep learning
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according to Home Office regulations Perform routine molecular biology techniques and prepare samples for downstream analyses such as flow cytometry, immunofluorescence and sequencing Conduct 2D and 3D cell
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Multiple Long-Term Conditions (MLTCs). It has a particular focus on research that is highly relevant to disadvantaged communities who are most impacted by MLTCs. It is led by a multidisciplinary team of
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capability Broad understanding of next generation sequencing (NGS) technologies and demonstration of proven experience in analysis of bulk and single cell transcriptomic data would be an advantage Ability