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Field
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methodology to deliver simulations over a wider operating regime than currently possible. The realisation of this aim involves the advancement of the modelling of the air film and the foil, and their
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PhD studentship: Improving reliability of medical processes using system modelling and Artificial Intelligence techniques Supervised by: Rasa Remenyte-Prescott (Faculty of Engineering, Resilience
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with analysis to understand complex phenomena in high-speed boundary layers, from transonic up to hypersonic. This is important for next-generation flight vehicles, including atmospheric entry craft
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to the development of multiscale computational models for simulating crack propagation and establishing reliable methods to predict the residual strength of composite structures. The simulations, performed in Ansys
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wave power without emissions. In the PhD you will have the opportunity to explore and develop novel renewable energy harvesting systems and devise and develop new modelling approaches and conduct
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aforementioned tasks with the following actions: Develop the principles and theories for governing the scalability principles for building innovative robotics end-effectors that can access geometrically complex
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an assessment of the part quality; this will involve the development of laser beam processing on specific aerospace materials, and a model to understand the fundamental mechanisms of the process to identify
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of mechanical and robotic systems •Ability to use finite element modelling and to simulate complex mechatronics •Ability to implement control and kinematics with hardware-in-the–loop •Background with relevant
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combination of academic and industrial challenges which will enhance the student’s ability to tackle complex intellectual and practical aspects of computer vision and robotics. We are seeking talented
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powerful framework for decentralised machine learning. FL enables multiple entities to collaboratively train a global machine learning model without sharing their private data, thus enhancing privacy