Sort by
Refine Your Search
-
This self-funded PhD opportunity explores assured multi-sensor localisation in 6G terrestrial and non-terrestrial networks (TN–NTN), combining GNSS positioning, inertial systems, and vision-based
-
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
-
. Funding This is a self-funded PhD, open to UK, EU and International applicants. Diversity and Inclusion at Cranfield We are committed to fostering equity, diversity, and inclusion in our CDT program, and
-
AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
We are pleased to announce a self-funded PhD opportunity for Quantitative assessment of damage in composite materials due to high velocity impacts using AI techniques. Composite materials, such as
-
of the student’s project and allow them to network. The partners will also be able to offer the PhD student the opportunity of placements, during which the student can gain major insights. This would help them
-
events, alongside our Doctoral Researchers Core Development programme (transferable skills training), provide those studying a research degree with a wealth of social and networking opportunities. How
-
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
-
provided. Join us to modernise one of the world’s most sustainable water-treatment technologies for a net-zero future. At a glance Application deadline10 Dec 2025 Award type(s)PhD Start date26 Jan 2026
-
members of the Business Disability Forum and Stonewall University Champions Programme. Cranfield Doctoral Network Research students at Cranfield benefit from being part of a dynamic, focused and
-
This self-funded PhD research project aims to advance the emerging research topics on physics-informed machine learning techniques with the targeted application on predictive maintenance (PdM