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Research Groups at the Faculty of Engineering, which conduct cutting-edge research into electric propulsion systems, composite materials, and advanced simulation technologies. Vision We are seeking a highly
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engineering knowledge. Funding support We are offering home fees and UKRI minimum stipend. Further information Please contact Tao Yang ezzty@exmail.nottingham.ac.uk for further information Closing date 16
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of innovative computational methods using Big Data, Behavioural Science and Machine Learning to understand behaviour through the lens of digital footprint/“smart data” datasets, cutting across sectors ranging
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fit if you: Have a background in Biochemistry, biomedical sciences, organic chemistry and biophysics Are desire to explore the biochemical composition and biological activity of GAGs Are confident
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to the analysis of time series. In particular, the project 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
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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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Computation and Data Driven Design of Materials for Onboard Ammonia Cracking This exciting opportunity is based within the Advanced Materials Research Group at the Faculty of Engineering which
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computer literacy, good inter-personal communications skills. Desirable skills: A Master in Health Economics with experience in cost effective analyses. Funding notes The three year studentship covers UK
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-edge advancements in automated drug discovery through the integration of high data-density reaction/bioanalysis techniques, organic synthesis, laboratory automation & robotics and machine learning
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the Manufacturing Technology Centre (MTC). It is based within the Advanced Manufacturing Technology Research Group (AMTG) at the Faculty of Engineering, University of Nottingham, which amongst its wide research