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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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(SFDI) and also from our custom-built photoplethysmography (PPG) sensor. Applicant should have experience in time-series processing with appropriate AI models (recurrent networks, LSTM) and experience in
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PhD project: Modelling Resilience of Water Distribution Networks Supervised by Rasa Remenyte-Prescott (Faculty of Engineering) Aim: To develop an modelling approach for assessing water network
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, the University of Nottingham provides the perfect environment to carry out high-impact research that can genuinely shape the UK’s Net Zero future. This is your opportunity to create breakthrough materials
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the perfect environment to carry out high-impact research that can genuinely shape the UK’s Net Zero future. This is your opportunity to create breakthrough materials for clean energy technologies and develop
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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
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to produce anti-counterfeit markings, dye-free colour images, humidity and chemical sensors, anti-glare coatings and optical filters. This project will develop additive manufacturing of devices with actively
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the University of Nottingham, contributing to cutting edge research into clean and sustainable energy technologies. Vision and Aim Ammonia is an essential component of the global strategy to achieve net-zero
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Research Group) Aim: Model and analyse component and system level reliability, availability and resilience of hydrogen systems Background Driven by climate change concerns and net-zero carbon targets, in
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for an enthusiastic graduate to build a strong skillset in interdisciplinary research and a collaborative network with both academic and industrial partners at an international level. Important funding note: This