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on a new project called TRUSTLINE, which is part of the Learning Introspective Control (LINC) DARPA Program. The project aims to develop machine learning (ML)--based introspection and monitoring
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by the EPSRC. The research will involve developing new controlled polymerization catalysts to deliver carbon dioxide-derived and bio-derived polymers. The catalysts, and processes, used to make
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the lead of Prof Daniela Ferreira conducts research using controlled human challenge models to better understand mucosal immunity to respiratory pathogens and inform vaccine development. This position is
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of genome organisation and metabolic control—with the bold vision of building synthetic life. In this role, you will develop and apply computational methods to analyse single-cell modalities, focusing on gene
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on understanding the spread and control of human infectious diseases using modelling and pathogen genomics. This is a short-term opportunity to apply machine learning methods to two key projects. First, you will
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at command line and BASH scripting Experience working with large scale, complex datasets and data wrangling skills Strong publication record and familiarity with the existing literature and research in
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at command line and BASH scripting Experience working with large scale, complex datasets and data wrangling skills Strong publication record and familiarity with the existing literature and research in
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simple) optical design and assembly, larger practical whole system design, system control and automation (lab and industrial), automating data acquisition, data processing and interpretation. It may be
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the lead of Prof Daniela Ferreira conducts research using controlled human challenge models to better understand mucosal immunity to respiratory pathogens and inform vaccine development. This position is
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that control the response to low oxygen conditions in Marchantia polymorpha. They will contribute both to the practical work with plants but also some bioinformatics work on protein structure and function