478 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "UNIV" PhD scholarships in United Kingdom
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support population-level prevention strategies. Further information: Applicants should have either a minimum 2.1 undergraduate degree in a relevant area (public health, epidemiology, economics, statistics
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-based modelling with data driven surrogate approaches. The first stage of the project will involve the development of a multi-layer hydrodynamic model capable of representing key three-dimensional flow
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their suitability for real-time emergency decision-making. This project addresses this challenge by combining physics-based modelling with data driven surrogate approaches. The first stage of the project will involve
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, ion beam transport and detection, laser spectroscopy for atomic and nuclear physics, high voltage systems, and data analysis. The minimum academic entry requirement for a PhD in the Faculty of Science
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This is an exciting PhD opportunity to develop innovative AI and computer vision tools to automate the identification and monitoring of UK pollinators from images and videos. Working at
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. You will gain experience in laboratory skills, hands-on design and manufacture of components, advanced data analysis and programming skills. You will work as part of a cohesive friendly team and as a
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temperature interaction is not fully understood. This information is important as it can be used to inform new product formulations. Therefore, the aim of this Master of Research is to gain a fundamental
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English language requirements . Funding information: The studentship is for 3 years and provides a minimum tax-free stipend of £20,780 per annum (2025/26 rate) for the duration of the studentship plus university
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an advantage. English language requirements: Applicants must meet the minimum English language requirements . Funding information: The studentship is for 3 years and provides a minimum tax-free stipend
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-edge adaptive mesh refinement techniques; a lightweight prediction tool developed upon the simulation data to predict key thermofluidic parameters for the design of high heat flux cooling components