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Field
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-funded Imaging Machine learning And Genetics in Neurodevelopment (IMAGINE) lab, in the Research Department of Biomedical Computing. The post will benefit from the extensive and broad expertise in AI and
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. This PhD project aims to create advanced XCT workflows by developing Artificial Intelligence (AI) and Machine Learning (ML) tools to support imaging before the reconstruction phase. The research will focus
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, and space hardware. This PhD research aims to develop a comprehensive Mode Selection Framework for Reduced Order Modelling (ROM) in Structural Dynamics—using machine learning to build robust
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hazardous or harmful knowledge from collaboratively trained models, positioning the work within the broader trustworthy AI agenda. The project sits at the intersection of privacy-preserving machine learning
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with NEOM, one of the world’s largest ecological restoration programmes, the project will develop machine-learning approaches to analyse satellite observations of vegetation change and evaluate large
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Engineering, Mechatronics, or Robotics, with a heavy emphasis on dynamic system theory, or a closely related discipline. Strong academic background in applied intelligent control techniques, machine learning
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, machine learning, and photonics. Be part of a multidisciplinary research team spanning science and engineering. Access state-of-the-art laboratories and high-performance computing facilities. Gain
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harvesting recent breakthroughs in Machine Learning (ML) and analytical modelling. Specifically, this project seeks to quantify key performance metrics and create powerful adaptive ML-driven management methods
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framework integrating physics-informed machine learning, scenario generation, and human-in-the-loop preference-based reinforcement learning to prioritise climate-robust and equity-aligned interventions
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programming (e.g., Python/C++), machine learning frameworks, or robotics software environments such as ROS. You are motivated to work in a multi-disciplinary research environment combining engineering, AI, and