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-world impact of their research Be comfortable using and learning about quantitative data analysis *applicants with less experience of more advanced quantitative methods, such as computational modelling
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highly desirable but not necessary as training will be provided. Experience of computer modelling is desirable but not essential as full training will be provided in an active and well-resourced research
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demonstrated by broader professional and life experiences. This evidence should be centred on your ability to undertake and complete a PhD and contribute to a positive PhD community. If English isn't your first
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Fully Funded PhD Research Studentship tax-free stipend of £20,870 Design, Informatics and Business Fully Funded PhD Research Studentship Project Title: Autonomous AI-powered red agents for enhanced
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The Institute for Energy Systems at the School of Engineering of the University of Edinburgh is looking for enthusiastic, self-motivated applicants for an exciting PhD position that will research
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treatment processes through advanced machine learning, validated against physics-based models and experimental data. 2. System Integration: Integrating the DTs into material and energy balance equations
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2025 Reference: RD-PHD-01-LS-MH-25 Project Title: The cost of silage production: measuring the carbon footprint of silage Primary supervisor: Prof. Liam Sinclair Co supervisors: Dr Kate Le Cocq, Dr
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; EPSRC Centre for Doctoral Training in Green Industrial Futures | Edinburgh, Scotland | United Kingdom | 2 months ago
This is a fully-funded 4 year PhD offering an annual tax-free stipend of £20,780, tuition fees and an enhanced research and training grant. This PhD is one of a number of projects hosted by
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. How to apply Applications should be submitted via the Veterinary Medicine and Science PhD programme page, by clicking the 'Apply' button, above. In place of a research proposal, you should upload a
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techniques that are useful for the modelling of many real-life systems. These include the development and analysis of stochastic models, computer simulations, differential equations, statistical inference