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generate a new understanding of MoSS. These advances will have applications across multiple sectors, including pharmaceuticals, agrochemicals, and food industries. There are multiple opportunities
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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
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, characterisation and modelling to study and generate a new understanding of MoSS. These advances will have applications across multiple sectors, including pharmaceuticals, agrochemicals, and food industries
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learning architectures including generative models, particularly for sequence or structural data (e.g. transformers, graph neural networks) Proved experience in working independently and as part of a
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focuses on improving our understanding of multiple sclerosis disease progression and how treatment can impact progression. This work will focus on the unique Novartis Oxford MS (NO.MS) dataset, the largest
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predictive analysis for Novartis Oxford collaboration for AI in medicine. The collaboration focuses on improving our understanding of multiple sclerosis disease progression and how treatment can impact
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the direction of Professor Pinar Ozcan, gives the successful candidate the opportunity to join at the start of the project. It will examine how organisations can align profitability with consumer well-being. Many
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contribute to the study and sequencing design, collaborate with experimental biologists to validate the hypotheses, and advise and supervise computational staff and students. You will write research articles
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, including next generation sequencing is essential. The successful candidate will be highly motivated and enthusiastic with an outstanding academic track record, good communication skills and the ability
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(e.g. nanopore-based sequencing, reverse genetics, immunoblotting, luciferase-based assays, RT-qPCR, microscopy) to study virus evolution and innate/adaptive immunity in the context of viral infection