37 software-engineering-model-driven-engineering-phd-position-"https:" PhD positions at Cranfield University
Sort by
Refine Your Search
-
postharvest drying energy demand. Combining applied mycology, food safety modelling, precision agriculture and Net Zero energy systems, the research will deliver energy-efficient, data-driven grain storage
-
Advances in computing, experiments, and information will continue to reshape engineering in the next decade. This PhD position will nurture a multidisciplinary innovator with the tools to unravel
-
electronic systems. This PhD project aims to develop intelligent electronic systems capable of autonomous fault detection and self-repair. The research will investigate AI-driven methodologies for predictive
-
The research in this doctoral opportunity will develop a failure model that can represent the combined effect of surface and bending failures in gears to perform reliable health prognostics. Lack
-
. At a glance Application deadline01 Apr 2026 Award type(s)PhD Start date01 Jun 2026 EligibilityUK, EU, Rest of world Reference numberSATM606 Entry requirements Applicants should have an equivalent
-
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
-
and controlling defects and lay the foundation for a thermal physics-based approach to process qualification. Additive manufacturing (AM) is a rapidly evolving technology that continues to drive
-
, reduce monitoring burden, and enable proactive, cost-effective compliance with future PFAS standards. The aim of this research is to develop a mechanistic-driven multicomponent model to predict PFAS
-
complex in scenarios like parallel-pass deposition, thin-wall deposition, and off-centre or out-of-position deposition. Additionally, FEA models are focused on thermal conduction in solid medium and often
-
mechanism. The integrating should enable to guarantee certain properties of the learned functions, while keep leveraging the strength of the data-driven modelling. Most of, if not all, the traditional