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; EPSRC Centre for Doctoral Training in Green Industrial Futures | Bath, England | United Kingdom | 3 months ago
. The research will be computational based, and at this stage is still broad, so we can formulate the optimal plan for the right candidate. We will take an interdisciplinary approach, and you will be able
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; EPSRC Centre for Doctoral Training in Green Industrial Futures | Edinburgh, Scotland | United Kingdom | 3 months ago
, preparing them for a successful career in academia, industry, or entrepreneurship. Click Apply for more information on our application process. Deadline: 30th June.
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control system that enhances Annual Energy Production (AEP), reduces mechanical stress, and improves fault detection using machine learning (ML) and physics-based modelling. The candidate will gain hands
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elements like Physical Unclonable Functions (PUFs) and True Random Number Generators (TRNGs) to secure hardware components. Embedded Trust Protocols: Design protocols that establish and maintain trust within
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on the application process, please contact the School's HR Team on mmllpersonnel@admin.cam.ac.uk . The closing date for applications is midnight (BST) on Sunday 15 June 2025. Click the 'Apply' button below to register
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science, mechanical engineering or physics. This interdisciplinary project requires a passion for both experimental work and computational modelling, along with a keen interest in learning fracture
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explore or optimise the flexible structures and manufacturing process of Litz wires. This studentship offers the opportunity for the PhD student to lead the development of innovative simulation tools
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and modelling techniques. Real-World Impact: Contribute to transformative technologies in clean energy and carbon capture. Future job opportunities: Digital modelling and computational fluid dynamics
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the two triage methods. Yorkshire Ambulance Service NHS Trust is in the process of migrating from AMPDS to NHS Pathways, which presents a unique opportunity to determine which tool is the most appropriate
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to achieve efficient and proactive reconstruction of the printing process, enabling real-time in-situ monitoring of large-volume material deposition and 2) How to adaptively compensate for size-induced defect