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microbiology, and machine learning, you will identify AMR genes, pathogens of public health concern (including ESKAPE and WHO-priority organisms), and reconstruct metagenome-assembled genomes (MAGs). Across five
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machine learning. This particular thematic area will be supervised by Associate Professor Agni Orfanoudaki. You will be responsible for planning and managing your own research programme within
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background in mathematics, statistics, population genetics, phylogenetics, epidemiological modelling, or machine learning. Highly motivated candidates with some, but not all, of the skills requested will be
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• Previous publications related to single cell sequencing analysis and mouse genetics • Previous experience in programming in Python, and/or application of machine learning for analysing data
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administrative processes. • Good computer literacy; ability to word-process, use email, manipulate databases and spreadsheets, and familiarity with Microsoft Office • Excellent attention to detail
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navigation algorithms and machine learning models on physical robot platforms. We are particularly interested in candidates with expertise in generative AI and curriculum learning applied to robotics, as
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research for understanding the learned algorithms in brains and machines. The post holder will provide guidance to less experienced members of the research group, including postdocs, research assistants
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responsibility for carrying out research for understanding the learned algorithms in brains and machines. The post holder will provide guidance to less experienced members of the research group, including postdocs
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and apply state-of-the-art modelling, characterisation, and machine learning techniques to understand how batteries behave and age. Collaborating with project partners, you’ll turn these insights
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leave • A comprehensive range of childcare services • Family leave schemes • Cycle and electric car loan schemes • Employee Assistance Programme • Membership to a variety of