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developed by University Hospital Sussex in collaboration with their industrial partner, and which makes it possible to collect data from both layers of the heart at the same time. Key aims of this project
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will examine and develop methods that go beyond the Markovian paradigm. It will consider a range of time series data, focusing on those that show challenging properties of uncertainty, irregularity and
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/developing-people-and-skills/stfc/training/studentship-information-for-students/payments-to-students/ ). An additional Research training support grant totalling £5,000 is available for the duration
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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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, 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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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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| £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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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