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performance simulation capabilities for gas turbine engines developed at Cranfield University as the starting point. Applications are invited for a PhD studentship in the Centre for Propulsion and Thermal Power
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of: An R package for path-space rejection sampling for diffusion processes An R package for Bayesian Fusion pooling inference, such as privacy Bayesian Fusion and constraint Bayesian Fusion The position is
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, please contact the supervisory team directly to find out more and discuss a project proposal before making an application to the lead university. You should indicate clearly on your application which
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High Performance Computing facility, where the current code is implemented. The candidate will, among other activities, extend the model to treat different management interventions, peat growth and decay
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at the interface between stochastic modelling, signal processing and data science. Ultimately, the project will develop key indices that can be used to assess the health of the soil ecosystem. Such indices
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potential for research and publication at the highest level, communication skills, and a good understanding of and interest in the policy process. Salary: Research Associate: £37,694 -£46,049 (Grade 7
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requirements of the role. Please note referees may be contacted at any point in the assessment process. If you upload any additional documents which have not been requested, we will not be able to consider
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microenvironment (TME), affecting immune cell signalling in ways that can either promote or inhibit tumour growth. While several clinical and preclinical trials have explored radioimmunotherapy, success has been
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This self-funded PhD opportunity sits at the intersection of several research domains: multi-modal positioning, navigation and timing (PNT) systems, AI-enhanced data analytics and signal processing
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the interpretability of these models can be enhanced to support clinical decision-making. This project will leverage the complementary expertise of both supervisory teams in EEG signal processing, graph deep learning