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. The enhanced image quality will support earlier and more reliable detection of eye diseases. Combining artificial intelligence with mathematical modelling, this non-invasive, cost-effective approach has
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
. This PhD project will tackle that challenge by developing intelligent methods that combine AI techniques such as language models that interpret technical text and knowledge graphs that map engineering
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type(s)PhD Duration of award3 years EligibilityUK, EU, Rest of world Supervisor Dr. Gustavo Castelluccio is a leader in Mesoscale Mechanics. His work integrates multiscale models and experiments
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manufacturing (year 1) - Advanced Composites manufacturing using energy absorbing fibres and nanomaterials (year 1) - Analytical/mathematical modelling and FEA modelling of hyper-velocity impact test of
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with engineering, physics, mathematics, acoustics, fluids, electronics or instrumentation background. Prior experience in computational modelling is beneficial, but not mandatory. Similarly, experience
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, multidisciplinary PhD research projects across areas such as: Zero Emission Technologies. Ultra Efficient Aircraft, Propulsion, Aerodynamics, Structures and Systems. Aerospace Materials, Manufacturing, and Life Cycle
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University. Applications are invited for a PhD studentship in the Centre for Propulsion and Thermal Power Engineering, Cranfield University, in the area of gas turbine performance, diagnostics and prognostics
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aircraft. While working on this exciting research project, you will be provided with: A fully funded 4 year full-time PhD - £24,000 tax-free stipend per year. Attendance/presentations to international and
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unbounded variable and instance sets. In addition, novel approaches such as Physics Informed/Guided Learning allows the learning models to capture the underlying physics/patterns and to generate physically
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of big data might not be possible to be captured by traditional modelling approaches. This implies that mathematical modelling of such data is infeasible. The data-driven modelling approach could resolve