534 cloud-computing-"https:"-"https:"-"https:"-"https:" PhD positions in United Kingdom
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learning/AI, geoarchaeology, environmental science, or computer science would be beneficial, but is not required, depending on equivalent experience. Funding notes: This project is funded by the European
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at the University of Edinburgh. Candidates currently studying outside of China are eligible to apply, but not those who have already commenced a PhD programme of study prior to the 2025-2026 academic year. The
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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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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
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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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: 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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. There is flexibility to tailor the research to your strengths and interests. Funding This fully funded Connected Waters Leverhulme Doctoral programme studentship is sponsored by the Leverhulme Trust and
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for the potential programme you are applying for.