Sort by
Refine Your Search
-
relevance. A digital twin framework for safe, simulation-based validation before deployment in operational wind farms. Develop explainable AI (XAI) frameworks and human-computer interfaces that enable wind
-
of award3 years EligibilityUK, EU, Rest of world Entry requirements Applicants should have an equivalent of first or second class UK honours degree or equivalent in a related discipline, science (chemistry
-
this multidisciplinary project, the student will develop expertise in: Direct collaboration with leading medical imaging and materials science partners. Access to Cranfield’s advanced materials characterisation and
-
in our CDT program, and warmly encourage applications from students of all backgrounds, including those from underrepresented groups. We particularly welcome students with disabilities, neurodiverse
-
: • Experience with programming (Python, MATLAB), • background in aerospace, computer science, robotics, or electrical engineering graduates, • hands on skills in implementation of fusion
-
. The integration of AI into hardware not only enhances performance but also reduces energy consumption, addressing the growing demand for sustainable and efficient computing solutions. This PhD project delves
-
design, technology and management expertise. We link fundamental materials research with manufacturing to develop novel technologies and improve the science base of manufacturing research. The Integrated
-
reduces crack propagation in composites, reduce failure due to delamination and significantly improves fracture toughness [Williams et al, Journal of Materials Science 48, 3, 1005-1013, 2013]. In addition