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Field
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the direction of Dr Maslovskaya, you will be part of a large multi-institution team of survey data collection methods experts in the UK. You will analyse probability-based NatCen online panel data collected using
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of Biological Sciences is a large and multidisciplinary team of more than 80 high-calibre academic and professional staff delivering world-class education and research programs to approximately 600 undergraduate
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collaboration that has mapped emotional hotspots in four cities in the UK and EU using spatial analysis of social media data. The next phase of the project aims to incorporate explainable AI (XAI) to interpret
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working will be considered. Contract type: Permanent Salary: Full time starting salary is normally in the range of £37,337 to £44,906 per annum, with potential progression up to £51,298 per annum; If PhD is
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tenable for up to 4-years. Applications are welcome from graduates of any university. Candidates will usually have completed their PhD, but must not have undertaken more than 3-years of postdoctoral work by
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Medicine Hub for Applied Bioinformatics). We are looking for an ambitious candidate with established expertise in bioinformatics, specifically dealing with large data sets and data integration. Knowledge
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community. Required Skills and Qualifications: Minimum PhD in computer science/ engineering or related fields. Proficiency in Python is a must. Experience with other programming languages such as Java, C/C
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visualization, geospatial analysis, and processing of large amounts of meteorological and remote sensing data. Background Investigation Statement: Prior to hiring, the final candidate(s) must successfully pass a
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, large-grant project on the epidemiology of bovine tuberculosis in wild badgers, using state-of-the-art Bayesian modelling approaches to study the drivers of infectiousness and transmission of infection in
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fully resolved single-fracture simulations to large-scale discrete fracture network (DFN) upscaling. Your work will directly address high-value industry challenges in energy and resource systems. CML is