33 embedded-system-"https:"-"https:"-"https:"-"https:"-"U.S" PhD positions at Cranfield University
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will examine how the configuration, connectivity and condition of these dynamic water systems, and their surrounding land cover, influence environmental buffering, biodiversity and social benefits
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Are you passionate about developing novel research and keen to shape the future of energy transfer technologies in areas such as, forensic science and Uncrewed Aerial Systems (UAS). We
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This is a self-funded PhD position to work with Dr Adnan Syed in the Surface Engineering and Precision Centre. The PhD project will focus studying high temperature corrosion mechanisms in details
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap
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and kinematic models with machine-learning-based channel state information (CSI) prediction to enable robust, low-latency connectivity across multi-layer NTN systems. This PhD project sits
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candidate would have experience with computational modelling and control of dynamical systems. Other useful skills include scientific programming (e.g., Python or Matlab), control system design, and
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This funded PhD studentship is an exciting opportunity to conduct new social-ecological research on perceptions of urban blue space. Despite often appearing to be natural systems, urban blue space
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stakeholders, with an interest for industrial research. Funding This is a self-funded research opportunity. The cost for running the composites manufacturing and testing experiments and facilities cost will be
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landscapes and emerging stressors. With opportunities for field or semi-field studies and close collaboration with leading academic and industry partners, this project is ideal for candidates who want
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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