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, particularly for sequence or structural data (e.g. transformers, graph neural networks, diffusion models) Proved experience in working independently and as part of a multidisciplinary team Evidence of strong
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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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proficient with microbial genetics, molecular microbiology, basic tissue culture and molecular biology techniques. Prior experience with bioinformatics (microbial sequencing and proteomics) would be
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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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sufficient specialist knowledge in transcriptomics and NGS sequencing as well as the essential computational skills (R, Unix or Python) to develop research programmes in this area, and familiarity with
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pipeline materials. You will perform microbial community profiling using 16S rRNA gene sequencing to identify microbial groups that have been associated with MIC (e.g. sulphate-reducing bacteria, methanogens
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interactive learning. Specifically, you will design and implement a web-based graphical user interface (GUI) that enables real-time in-class interaction between students and teachers across multiple locations
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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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may have a detrimental impact on pipeline materials. You will perform microbial community profiling using 16S rRNA gene sequencing to identify microbial groups that have been associated with MIC (e.g
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the collection of high-quality, synchronous, multi-camera video data across multiple exclusive real-world environments (capture volumes). Each capture volume requires a minimum of 24 cameras, and you will collect