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Field
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(WP1) of the SHEILD network “Clinical aspects of implant-associated infection” which has the following overall objectives: determine the pathogenesis of implant associated infections (IAIs) and adverse
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Research Objectives (OBJs) include OBJ1- Modelling framework design, development, and validation of models for the eVTOL and its sub-systems for both control development and evaluation. OBJ2- Design and
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backgrounds such as in computer science, mathematics (pure or applied), or engineering. The successful applicant will be highly motivated, have excellent time management, and a proven track record in
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rated as ‘world-leading’ or ‘internationally excellent’. This PhD will be based within the Centre for Space Systems and the Resilient PNT research group, both of which have strong track records in
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climate change-resilient infrastructure slopes. This PhD is co-funded and co-supervised by Network Rail. The aim is to enhance understanding of how drainage systems impact slope hydromechanical behaviour
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technology policies and AI-powered threats fundamentally reshape alliance structures and power dynamics. The second track analyses the geoeconomics of AI through a non-zero-sum lens, revealing how resource
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for platelet production, behave in the body using advanced imaging techniques. Using genetically modified mice that allow real-time tracking of cellular signalling, the project will explore how megakaryocytes
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have access to world-class laboratories, industrial-scale deposition systems and unique characterisation facilities. With a strong track record in energy innovation and industry partnerships
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in AI and machine learning – from classical approaches to large language models. You are proficient in Python and key ML libraries (e.g. scikit-learn, PyTorch, LLM APIs), and you have a track record of
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laboratories, industrial-scale deposition systems and unique characterisation facilities. With a strong track record in energy innovation and industry partnerships, the University of Nottingham provides