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PhD at the Forefront of Computational Solid Mechanics and Machine Learning School of Mechanical, Aerospace and Civil Engineering PhD Research Project Self Funded Dr J L Curiel Sosa Application
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Educational NLP with Multi-Agent and Multimodal LLMs for Inclusive and Collaborative Learning School of Computer Science PhD Research Project Directly Funded Students Worldwide Dr Zheng Yuan
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(Python, C++). - Knowledge of AI, machine learning, control systems, or reinforcement learning. - Ability to work independently, communicate effectively, and contribute to collaborative research. Desirable
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parallel processing, FPGA coding and analysis, along with Machine Learning and AI based image analysis. The final aim of the project will be to generate in-situ / live film profile data to coating line
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Design of a Fault Detection System for AI-Assisted Adversarial Attacks on Industrial Control Systems
AI-assisted adversarial attacks. You will work on topics such as cybersecurity, intrusion detection, adversarial machine learning, industrial automation, digital twin technology, and reinforcement
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interpretable machine learning (IML) and nonlinear system identification approaches. In doing so, we will build transparent, interpretable, parsimonious and simulatable (TIPS) models to help identify the causes
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to completion of a PhD) in engineering/appliedmathematics or a related area or have relevant equivalent experience. Essential Application Experience in the following skill areas: Machine learning and its
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preferences for them using birds as a model system. Capitalising on recent advances in computational neuroscience and machine learning, specific objectives are to (1) quantify common design features of avian
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. This project will develop responsive manufacturing technology that will have sufficient flexibility to overcome such problems by utilizing intelligent machine learning to control the printing process in real
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are looking for an ambitious candidate with a strong background in mathematical and statistical methods for both physics-based modelling and machine learning, and their application to engineering problems in