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Field
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(computer vision technologies). The interdisciplinary nature of this PhD will require the integration of environmental science, engineering, and community science methodologies. Supervisors: Primary
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the “Dialling up Performance for on Demand Manufacturing” Programme Grant, which will place the student within an active and supportive team of 9 other PhD students, 15 postdoctoral researchers, 18 world-leading
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the “Dialling up Performance for on Demand Manufacturing” Programme Grant, which will place the student within an active and supportive team of 9 other PhD students, 15 postdoctoral researchers, 18 world-leading
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engineering, or atmospheric science* Expertise in and passion for computational modelling and software development/engineering Expertise in cloud physics or contrails preferred but not required Creative problem
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-class or 2:1 (or international equivalent) Master’s degree in Computer Science, Robotics, Mechatronics or Electronic/Electrical Engineering, or a related field. • Knowledge of machine learning/deep
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fundamental engineering science with advanced experimentation to develop methods that inform and validate next-generation tyre friction models. Key objectives include: Developing innovative experimental
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and optimization of machine learning methods. Candidate’s profile An ideal candidate would typically have: a strong degree or higher qualification in a relevant field (e.g. computer science, mathematics
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Deadline: 7th January 2026 One fully funded, full-time PhD position to work with Dr. Viacheslav Borovitskiy in his new research group at the School of Informatics, University of Edinburgh. Our
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Job Purpose You will contribute to/ lead a project “Comprehensive assessment of the biology and public health importance of Anopheles stephensi in Africa (AnoSTEP Africa)” working with Prof Heather
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the “Dialling up Performance for on Demand Manufacturing” Programme Grant, which will place the student within an active and supportive team of 9 other PhD students, 15 postdoctoral researchers, 18 world-leading