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, working with legacy print, manuscript, and digital sources. You will apply and adapt digital methods (especially TEI XML), analyse provenance data, disambiguate historical agents, and contribute
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Assistants is £32,546 - £35,116 and for Research Associates this is £37,174 - £45,413 per annum. Suitable candidates should have previous experience of genetic analysis of large scale genome sequence data and
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emissions, and enhance occupant health and wellbeing. As a Research Assistant, you will work closely with UK- and Egypt-based teams to analyse collected data, develop and test computer-based retrofit models
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several processing units with variable memory, can be profiled to pool the resources. The analytical systems, developed on data collected by onboard sensors and software triggers, can assist the operating
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environmental inputs, algae physiological parameters and microbial community eDNA data to develop predictive mechanistic models which can be utilised to develop an optimal cultivation strategy. The project is
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academia and industry. Uncover and quantify critical degradation mechanisms to inform the design of next-generation fusion materials. Translate sophisticated scientific data into impactful insights through
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| £20780 + £2500 industry top up (per annum (tax free)) Overview This exciting, fully-funded PhD opportunity invites applications from candidates with a robust foundation in data science, modelling, and
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detailed characterisation of the chemical processes controlling oxidation capacity in the tropical troposphere. The data collected will directly support international efforts to reduce uncertainties in
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products, including vaccines and novel therapy veterinary medicinal products. Your primary role will be to assess data submitted by pharmaceutical companies in support of their applications for new marketing
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the ability to simulate the temperature of the canopy-soil system, they provide valuable information on the interactions with the underlying surface. This is particularly important for urban areas where sparse