58 software-verification-computer-science Fellowship research jobs at University of Nottingham
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/Fellow who can deliver the research whilst helping to manage project delivery. Candidates must hold an appropriate engineering or science degree level qualification and a PhD (or be about to obtain a PhD
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the results of this project. Candidates must possess a good first Degree (or Master's) and PhD (or near competition) in Engineering, Mathematics, Physics, Computer Science, or related disciplines. Your working
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We seek to appoint a Postdoctoral Research Associate/Fellow to work in a vibrant and multidisciplinary project funded by the EPSRC Working with Centres Programme under the supervision of Dr Anabel
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flow in hybrid systems: improving the simulation of engineering structures”. As part of the project - jointly undertaken by the University of Nottingham and the University of Salford -, we offer a
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candidate will be responsible for the design, development, and execution of experiments, as well as data analysis and interpretation. This will involve using and adapting existing software, and developing new
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/Fellow who can deliver the research whilst helping to manage project delivery. Candidates must hold an appropriate social science degree level qualification and a PhD (or be about to obtain a PhD, which
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, Chemistry, Engineering, preferably in an area involving Raman spectroscopy (Applicants in the process of Ph.D. submission will be considered.) • Knowledge of Raman spectroscopy instrumentation. • Strong
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Applications are invited for the position of Senior Research Fellow/Research Fellow to contribute to an NIHR Programme grant study at the University of Nottingham. The successful candidate will work
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Facility at the University of Nottingham. The Facility is operated jointly by the Schools of Chemistry, Life Sciences, and Physics and Astronomy and comprises a 600 MHz DNP MAS NMR spectrometer equipped with
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entitled “White Matter Computation: Utilising axonal delays to sculpt network attractors”. The central aim of the project is to determine how dynamic patterns of neural activity evolve in a complex network