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al. eLife 2022) to extract information on the efficacy of antibiotics against bacterial populations and rapidly predict antimicrobial resistance. Moreover, by using omics, bioinformatics and AI
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bioinformatics approach to start to answer some of the questions we do not know in relation to equine EIPH. Supervisors:Dr Susan Armstrong and Professor Kalaman Jeevaratnam Entry requirements Open to candidates
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bioinformatics through the completion of the objectives. The supervisory team has complementary expertise providing multidisciplinary training to support the successful student candidate, who should be motivated
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sits between data collection and bioinformatics and data modelling This will require someone with an interest in data analysis and experience of scripting. The role will require working closely with
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of these complex, multi-omic single cell and spatial transcriptomics datasets and is therefore ideal for those with an interest in computational biology, bioinformatics, computer sciences and/or genomics
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PhD Studentship: Data-driven Probabilistic Modelling of Clonal Dynamics in Human Tissues and Cancers
. The project is therefore suitable for students with a background in (but not limited to): • Applied Mathematics or Theoretical Physics • Computational Biology or Bioinformatics • Computer Science or Engineering
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broad interdisciplinary training including (meta)genomic bioinformatics (esp. metabolic predictions and phylogenomics), novel microbial cultivation / isolation approaches, and genome engineering tools
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. The student will learn and work on computational research (data processing, data integration, data analysis including bioinformatics and AI using high-performance computing facilities), Clinical research
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or belief, sex or sexual orientation or social background. We value curiosity, independence of thought, plus an aptitude for research that combines laboratory work and bioinformatics. Entry
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products that form the basis of our current medical arsenal are often difficult to synthesize, purify, or engineer. In recent years bioinformatically predicted peptide antibiotics of RiPP group (ribosomally