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geological disposal facility, a highly engineered subterranean structure designed to store the waste for >100,000 years. Historically, two industrial scale powder processing routes have been used
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programme name, select ‘Electronic, Electrical & Systems Engineering’ and quote the advertised reference number ‘MA-2023-02584N’ in your application. Please submit a Curriculum Vitae (CV) and the minimum
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in one or more of the following areas: Organic synthesis Polymer chemistry Computer programming (Python) Submitting an application As well as supporting documents, applications must include a Research
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rate; international applicants must cover the difference between home and overseas fees The Department of Materials Science and Metallurgy at the University of Cambridge invites applications for a PhD
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
based within the Manufacturing, Materials and Design theme at the Centre for Digital and Design Engineering (CDDE), which offers access to advanced simulation, visualisation, and high-performance
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Engineering, Computer Science or related disciplines. Experience in autonomous system, manufacturing/robotics and machine vision development will be an advantage. To apply please contact the supervisor, Dr Kun
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edaphic conditions in this island group. Plant material is already available so the focus is on laboratory and computing techniques. During the placement you will be taught DNA extraction, DNA data handling
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overseas partners for collaborative projects on sustainable energy and bioelectronics. Interdisciplinary Skills Development: Bridge expertise in materials science, mechanical/chemical engineering, and
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engineering, manufacturing, economics and public perceptions. They will also develop the Diagnostic Discard exhibition to engage a broader public to consider diagnostic waste. The post-holder will be required
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computer science or mechanical engineering. The candidate will have programming experience, particularly on the development of machine learning pipelines. The University actively supports equality, diversity and