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Field
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap
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are not limited to: Data visualisation Text mining Network analysis Digital mapping 3D modelling Augmented/Virtual reality AI and computer vision Digital exhibitions and archives Scholarly communication by
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to the interests of one of the School’s research groups: Cyber-physical Health and Assistive Robotics Technologies Computational Optimisation and Learning Lab Computer Vision Lab Cyber Security Functional
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of Engineering), Mike Pound (Computer Vision, Computer Science Department), and Darren Wells (Plant and Crop Biophysics, School of Biosciences). Who we are looking for An enthusiastic, self-motivated, resourceful
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(particularly cognitive or applied psychology) Cognitive Science Human–Computer Interaction Engineering or Computer Science Health sciences Experience in empirical research, experimental design, data analysis
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Nancy and the long-standing experience in sophisticated computer simulation studies from Leipzig, promising unique prospects in advanced education of PhD students via research into this important field
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after employment. Required selection criteria You must have a relevant Master's degree in Reliability, Availability, Maintainability, and Safety (RAMS) or Cybernetics or Computer/Information Science with
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motivated and curious candidate with a background in computer engineering, embedded systems, or a closely related field. The ideal candidate has an interest in the intersection of artificial intelligence
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computer simulations by developing fundamentally innovative and advanced protection strategies. To enhance the reliability and safety of low-voltage networks with a high penetration of power-electronic
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reusable plaque–flow atlas. Key objectives include to: Develop automated computer aided design (CAD) and meshing pipelines to generate a library of arterial geometries representing common geometric