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be awarded a master’s degree in any of the following disciplines: Chemical Engineering, Physics, Mechanical Engineering, Energy Engineering, Environmental Engineering, Computer Science, or other
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deformation. Responsibilities Develop scientific machine learning methods in close collaboration with team members specializing in experimental techniques and materials science. Utilize unique experimental data
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£20,780 per year, and includes a 3-month fusion engineering CDT training programme as part of the 2026 Cohort. This project is co-supervised by Dr Chris Hardie from UKAEA. The UoB Materials for eXtremes
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Engineering, materials science or related subject. English language requirements: Applicants must meet the minimum English language requirements. Further details are available on the International website
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DTU Management’s Management Science division. The project is led by Professors Stefan Ropke and Richard Lusby and involves international collaboration with leading researchers in machine learning and
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of the Open Competition Domain Science-M programme (Twenty-one innovative research projects awarded through Open Competition Domain Science-M programme | NWO - https://www.nwo.nl/en/news/twenty-one-innovative
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interfacing of the sensors. The PhD candidate will work at the Integrated Devices and Systems (IDS) group within the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at the University
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degrees in either the natural sciences (chemistry, physics, mathematical/computational biology) or in the formal sciences (statistics, computer science, mathematics), but must have a serious interest in
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a Research and Technology Organization (RTO) active in the fields of materials, environment and IT. By transforming scientific knowledge into technologies, smart data and tools, LIST empowers citizens
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seek a proactive and enthusiastic individual with a first-class or upper second-class honours degree (or equivalent) in: Engineering (Mechanical, Materials, or related disciplines) Physics, Chemistry, or