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Field
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-efficient research that prevents fatigue failures has pushed towards integrated computational materials engineering approaches that improve competitiveness. These approaches rely on physics-based models
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the foundation of computer vision, monitoring, and control solutions. However, real applications of AI have typically been demonstrated under highly controlled conditions. Battery assembly processes can be
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research projects across areas such as: Zero Emission Technologies. Ultra Efficient Aircraft, Propulsion, Aerodynamics, Structures and Systems. Aerospace Materials, Manufacturing, and Life Cycle Analysis
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access to an NWSSDTP Research Training Support Grant for eligible research expenses. Application process Applications for this ESRC CASE PhD Studentship should be sent by email to the School of Law and
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Supervisors: Prof. Reinhard Maurer (Chemistry), Prof. Richard Beanland (Physics) Understanding how local atomic structure and long-range emergent magnetic and electronic properties in defective 2D
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models and physics-based models. More recently, hybrid prognostics approaches have been presented, attempting to leverage the advantages of combining the prognostics models in the aforementioned different
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with engineering, physics, mathematics, acoustics, fluids, electronics or instrumentation background. Prior experience in computational modelling is beneficial, but not mandatory. Similarly, experience
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to be considered. How to apply Apply online by clicking the 'Apply' button, above. Select programme type (Research), 2025/26, Faculty of Engineering and Physical Sciences, next page select “PhD
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Are We Looking For? We seek a proactive and enthusiastic individual with a first-class or upper second-class honours degree (or equivalent) in: Engineering (Mechanical, Materials, or related disciplines
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apply early for the best opportunity to be considered. How To Apply Apply online by clicking the 'Apply' button, above. Select programme type (Research), 2025/26, Faculty of Engineering and Physical