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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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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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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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, in collaboration with Rolls-Royce, will develop innovative coatings to safely contain hydrogen in critical aerospace materials through experimental and computational modelling work. You’ll join a
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materials in lateral flow cassettes, covering aspects from engineering, manufacturing, economics and public perceptions. They will also develop the Diagnostic Discard exhibition to engage a broader public to
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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
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: search for the ‘Course Title’ using the programme code: 8060F. select 'PhD Electrical and Electronic Engineering (full time)' as the programme of study You will then need to provide the following
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a first-class or upper second-class degree (or equivalent) in Civil, Mechanical, Structural and Marine engineering or other relevant areas. Programme Package and Funding Our EnerHy PhD Studentship
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; EPSRC Centre for Doctoral Training in Green Industrial Futures | Edinburgh, Scotland | United Kingdom | 2 months ago
. The project aims to reduce the carbon emissions associated with polymer waste, aligning with BASF’s sustainability goals. If successful, the outputs of this research could be implemented in industrial settings