172 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"Dr" PhD positions in United Kingdom
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strategy to improve the turbulence detection and quantification. The flow turbulence and velocity in a vascular flow phantom will be measured by Particle Image Velocimetry (PIV), against which MRI data will
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support a more controlled, data-informed manufacturing environment and improve structural performance across the full lifecycle of high-value engineered components. Aim The student will have opportunities
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. They include e.g. research and specialist skills training, communication skills, personal effectiveness, wellbeing training, impact and engagement, data and analysis and career planning. You will formally
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usability and accuracy, as well as conducting field tests to validate their effectiveness. Additionally, the research will explore the economic viability of these sensors to enhance real-time data collection
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: The funds for this post are available until 31 December 2028 in the first instance. To apply online for this vacancy and to view further information about the role, please click the 'Apply' button above
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. The project will also generate crucial data to the literature for more broadly informing public health initiatives in the UK. This PhD project will explore the following three research questions: What is the
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measurement and stopping rules controlling type-I error with finite-sample guarantees. Quantum statistical modeling: use of quantum relative entropy, Fisher information, and variational quantum circuits
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Studentship Information Supervisor: Vinay Shukla Subject Area: Plant & Crop Science Research Title: Root oxygen dynamics and development Research Description: The student will be part of a
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Doctoral Landscape Awards (IDLA) programme and is in partnership with Johnson Matthey. About HetSys: Harnessing Data, Modelling and Simulation for Real‑World Impact HetSys (Centre for Doctoral Training in
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the Faculty of Engineering, plus those housed in Plant Biosciences at the Sutton Bonnington campus. Data sets will be generated using simulated and experimental data and these will be used to train networks