45 computer-aided-manufacturing PhD positions at Cranfield University in United Kingdom
Sort by
Refine Your Search
-
and brain tissue mechanics to improve stroke treatment. Stroke is a leading cause of death and disability worldwide, making advancements in its diagnosis and treatment highly relevant. Computational
-
where it co-locates with the UK Race2Space programme. Each R2T2 studentship is associated with an industrial partner active in the launch industry, and provides full funding and stipend, an extensive
-
requirements A minimum of a 2:1 first degree in a relevant discipline/subject area (e.g. aerospace, automotive, mechanical, electrical, chemical, computing, and manufacturing) with a minimum 60% mark in
-
, multidisciplinary PhD research projects across areas such as: Zero Emission Technologies. Ultra Efficient Aircraft, Propulsion, Aerodynamics, Structures and Systems. Aerospace Materials, Manufacturing, and Life Cycle
-
developing novel composite materials with superior ballistic and hyper-velocity impact protection. The PhD research will help improve the reliability and longevity of space satellites. Composite materials have
-
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
-
. Our current research has an overarching focus on sustainability in and from space, working closely with industry and agency partners to deliver cutting-edge solutions for real-world problems
-
This is a self-funded opportunity relying on Computational Fluid Dynamics (CFD) and wind tunnel testing to further the design of porous airfoils with superior aerodynamic efficiency. Building
-
or industry) will need to provide their own financial support in relation to tuition fees, research support fees and living expenses. This studentship is open to both UK and International applications. Find out
-
algorithms are used that allow a computer to process large data-sets and learn patterns and behaviours, thus allowing them to respond when the same patterns are seen in new data. This include 'supervised