358 machine-learning "https:" "https:" "https:" "https:" "https:" "UCL" PhD positions in United Kingdom
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of lightweight, logic-based machine learning approaches. In addition, agents must support collective decision-making to achieve system-wide optimisation rather than isolated, local improvements. Finally
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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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, adaptive control strategies, and hybrid energy storage solutions to address key challenges in self-powered systems under dynamic environmental conditions by: Develop machine learning or heuristic-based
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and architectures that support efficient, secure, and scalable machine learning operations (MLOps) across resource-constrained environments for Edge AI. Ethical, and responsible FL for healthcare: In
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- 2025 - Business Strategy and the Environment - Wiley Online Library Additive Manufacturing: A Comprehensive Review Big data, machine learning, and digital twin assisted additive manufacturing: A review
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Wales, meaning most paediatric records are handwritten and unstructured. The project will prioritise digitising these records using natural language processing (NLP) and machine learning (ML) to create
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Super-Resolution Radial Fluctuations (SRRF) approach. The objective is to build machine-learning models that exploit the underlying physics of fluorescence fluctuations to deliver high-resolution, low
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high 2:1 in computer science, mechanical engineering, or aerospace engineering. You should have programming experience applied to physics/engineering problems and/or experience with machine learning and
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The accuracy and precision of opto-electronic 3D motion capture systems created a global shift towards lab-based biomechanical analysis over the last 30 years. With the advent of AI and machine
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establish a digital route to quantify the segregation behaviour of residual elements at austenite/austenite grain boundaries through atomic-scale simulations, using modern machine learning techniques and in