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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
engineers detect faults earlier, track system degradation, and make better-informed maintenance decisions. But how can we turn this complex information into something reliable, explainable, and actionable
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by the Ada Lovelace Centre and the University of Birmingham. This interdisciplinary project is ideal for candidates with a background in physics, materials science, chemistry, or computational science
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an increasingly complex development environment. Areas to consider that impact the modelling are: Framework Language Process How wide / how deep i.e. what do we model and why? How much provides a good answer i.e
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operation of autonomous systems in complex, real-world conditions. This PhD project aims to develop resilient Position, Navigation and Timing (PNT) systems for autonomous transport, addressing a critical
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into their structure. However, we don’t yet know how to design these complex printed parts and we are still developing new leading-edge materials to grant them new capabilities. Advantages: In this PhD studentship, you
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, integrating genetic, clinical, and demographic data for national research and trials. Establish high-fidelity MUC1 sequencing using long-range PCR and ultra-deep nanopore sequencing to resolve the complex VNTR
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Application deadline: 15/08/2025 Research theme: Computer Science No. of positions: 1 Eligible for: UK This 4-year PhD project will be funded by DLA studentship and is open to UK students
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fantastic opportunity for ambitious computer scientists to join our Computer Science Graduate Teaching Assistant (GTA) Programme! How does it work? Candidates will study for a four year, full time funded PhD
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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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essential to determine how these models can be made lightweight in terms of computational complexity, memory footprint, and energy consumption for deployment on edge devices or constrained gateways