Sort by
Refine Your Search
-
to arrange the tuition fees and living expenses. Find out more about fees here . Cranfield Doctoral Network Research students at Cranfield benefit from being part of a dynamic, focused and professional study
-
materials science and hydrogen technologies. The industrial sponsor, Airbus, is committed to net zero aviation by 2050 and is pioneering LH2 powered aircraft. This partnership provides a unique industrial
-
and life-cycle assessment will broaden your professional network while a dedicated training budget allows you to attend specialised courses—such as drone photogrammetry or advanced bioinformatics—and to
-
challenge in the UK's Net Zero transition. Current satellite dependent navigation remains vulnerable to interference, jamming and signal degradation, causing serious problems for safe and efficient transport
-
supporting the Net Zero 2050 target. This PhD project will develop an AI-enabled framework that optimizes wind turbine control and predictive maintenance. Using Deep Reinforcement Learning (DRL), the system
-
researcher with expertise in communication, project management, and leadership. You will build a robust national and international network and acquire advanced knowledge essential for implementing critical
-
. Cranfield Doctoral Network Research students at Cranfield benefit from being part of a dynamic, focused and professional study environment and all become valued members of the Cranfield Doctoral Network
-
modelling software. Practical experience in advanced manufacturing techniques for novel materials. Opportunities to present research at international conferences and build a professional network across
-
-informed data analytics tools for the predictive maintenance (PdM) strategy applications to high-value critical assets. Among others, the recently developed Physics-informed Neural Network (PINN) technique
-
at international conferences and build a professional network across academia and industry. Development of expertise in cutting-edge experimental techniques, computational modelling, and interdisciplinary