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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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stress analysis, manufacturing, hands-on oriented, digital image correlation, Abaqus, scripting How to apply: Stage 1: Submit your 2-page curriculum vitae (CV), transcripts and a 300-word statement
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Project Supervisor - Dr Jonathan Kirby The field of complex numbers is in many ways the most elegant and important example of a mathematical structure in model theory. It has been the prototype
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We are seeking a research assistant with a background in computing to develop AI models for image reconstruction from data from our ultra-thin fibre-based spatial frequency domain imaging device
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maximum of ONE student per project. This process will ensure an excellent fit of student to project and also an excellent strategic fit of the project within the faculty. Project titles: Bayesian methods
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to these questions will provide a new and much needed understanding of the controls of fluid-flow processes in the Cornubian batholith and their controls on the fluid mobilization of geothermal heat and metals
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results from human decision-making to inform the design of this new paradigm, and feed the results of the latter back into human decision-making to help make it more explainable. The PhD student will: (1
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to develop AI models for image reconstruction from data from our ultra-thin fibre-based spatial frequency domain imaging device (SFDI) and also from our custom-built photoplethysmography (PPG) sensor
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-inflammation and psychiatric disorders (e.g. depression). The cellular processes underlying structural brain change include neurogenesis and gliogenesis, cell loss, changes in cell shape, synapse formation and
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of the underlying the physical processes leading to predictability and forecast skill of North Atlantic cyclones. The project will also explore statistical post-processing methods to see if raw forecast skill can be