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and emerging applications, such as multi-domain autonomy and aerial mobility. With rising risks to PNT systems from interference, spoofing, and cyber-physical attacks, unified, security-aware integrity
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invaluable industry connections. Ideal for ambitious graduates seeking to combine technical excellence with commercial relevance in today's rapidly evolving industrial landscape. Smart technology integration
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integrating Machine Learning (ML) with physics-based degradation modelling will enhance early fault detection, reducing unplanned downtime. This PhD is hosted at Cranfield University, a global leader in
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. While autonomy is becoming more integrated into modern mobility, the reliability of Position, Navigation and Timing (PNT) systems—especially in environments where GNSS signals are denied or degraded
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, and help shape future funding and policy strategies in the UK and abroad. With this PhD, you will become an integral member of the EPSRC Centre for Doctoral Training in Water Infrastructure and
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This project aims to bridge the gap between technological advancements and their integration into societal and environmental systems by shifting from a product-centric to a service-oriented approach
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parameters, underlying material geometry and process environment). • Integrating process-dependent transferred arc energy distributions into an improved heat source model for FEA simulations. • Creating an FEA
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-efficient research that prevents fatigue failures has pushed towards integrated computational materials engineering approaches that improve competitiveness. These approaches rely on physics-based models
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pressure to reduce both energy demand and chemical consumption. Project SandSCAPE, an Ofwat-funded programme, tackles this challenge by integrating purpose-built robots that skim slow sand filter beds while
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evaluation. Prognostics is an essential part of condition-based maintenance (CBM), described as predicting the remaining useful life (RUL) of a system. It is also a key technology for an integrated vehicle