Sort by
Refine Your Search
-
community. We welcome students and staff from all backgrounds from over 100 countries and support our staff and students to realise their full potential, from academic achievement to mental and physical
-
habitat fragmentation. Working at the forefront of ecological modelling and movement ecology, you will build next-generation, process-based models to predict how real populations respond to complex
-
of the complex physics governing the interaction between the heat source and the material. Additionally, it seeks to develop an efficient modelling approach to accurately predict and control the temperature field
-
related discipline. This project would suit a candidate with a background in mechanical, control or aerospace engineering, physics, mathematics, or other relevant engineering/science degree. The ideal
-
AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
from all backgrounds from over 100 countries and support our staff and students to realise their full potential, from academic achievement to mental and physical wellbeing. We are committed
-
from over 100 countries and support our staff and students to realise their full potential, from academic achievement to mental and physical wellbeing. Cranfield Doctoral Network Research students
-
achievement to mental and physical wellbeing. Cranfield Doctoral Network Research students at Cranfield benefit from being part of a dynamic, focused and professional study environment and all become valued
-
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
-
physical wellbeing. We are committed to progressing the diversity and inclusion agenda, for example; gender diversity in Science, Technology, Engineering and Mathematics (STEM) through our Athena SWAN Bronze
-
Embark on a ground-breaking PhD project harnessing the power of Myopic Mean Field Games (MFG) and Multi-Agent Reinforced Learning (MARL) to delve into the dynamic world of evolving cyber-physical