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, and edge computing, AASs are evolving into smart, interconnected solutions for addressing the dynamic challenges of modern cities. This project investigates how to coordinate these multifunctional
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by detecting and predicting threats such as pests, diseases, and environmental stress in line with the UK Plant Biosecurity Strategy. The project harnesses computer vision, deep learning, and large
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: Comparative transcriptomics, orthology inference, positive selection detection, protein domain analysis, phylogenetic comparative methods Computational skills: UNIX/Linux, HPC computing, R, Python You will gain
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programming skills in Python/MATLAB, and an interest in digital twin technologies, cybersecurity and machine learning. Entry Requirements Acceptable first degree: Computer Science or related disciplines
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Project Supervisor - Professor Rudy Lapeer The Birth4Cast “Digital Twin” aims to create a subject-specific computational biomechanics simulator of childbirth from prenatal fetal MRI scans
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training programme by the University of Liverpool. As well as gaining a formal HE teaching qualification, you’ll learn key pedagogical skills for the academic job market, primarily through shadowing mentored
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also be involved in a fixed number of teaching hours during your time with us. In year one, alongside your specific PhD research modules, you’ll complete a short teacher training programme by
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biological materials. The development of novel computer-vision-based techniques for contactless detection, quantification, and prevention of sport injury. The development of robotic humanoid simulator and
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Institute, with close ties to the Department of Computer Science and Technology. Preferred skills/knowledge We are seeking a passionate and collaborative PhD student with a strong background in machine
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will be provided in algal culturing and physiological monitoring, as well as in molecular biology. Through the ARIES DTP and access to the University of Plymouth Researcher Development Programme, the PGR