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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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Research theme: Fluid Mechanics, Machine Learning, Ocean Waves, Ocean Environment, Renewable Energy, Nonlinear Systems How to apply: How many positions: 1 Funding will cover UK tuition fees and tax
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Project title: Privacy/Security Risks in Machine/Federated Learning systems Supervisory Team: Dr Han Wu Project description: In the wake of growing data privacy concerns and the enactment
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of dehydration using a low-power radio-frequency (RF) sensor. The research objectives include design optimization to improve wearability, robust data acquisition using machine learning and establishing correlation
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filled. This fully funded PhD explores AI-native and sensing-aware wireless systems where communications and sensing are co-designed end-to-end. You will unify modern machine learning, statistical signal
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insights from geometry and topology to discover new applications of machine learning. Multiple positions may be available. Role Requirements The successful candidate must have a PhD (or close to submitting
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for: A PhD in High Performance Computing, Machine Learning, Computer Science, Applied Mathematics, Data Science, or equivalent research, industrial or commercial experience Practical experience with
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treatment processes through advanced machine learning, validated against physics-based models and experimental data. System Integration: Integrating the DTs into material and energy balance equations
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to reconstruct subsurface defects; Implement image/signal‑processing or machine‑learning pipelines for automated flaw characterisation; Collaborate with the Federal University of Rio de Janeiro, including short
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technologies Artificial Intelligence and Machine Learning applications Computer Vision and sensor integration for agricultural applications Human-Robot Interaction in agricultural contexts Robotic Perception and