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Field
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Human Behaviour in projects targeting social good. Research at N/LAB focuses on the development and application of innovative computational methods using Big Data, Behavioural Science and Machine Learning
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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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untapped potential remains in extracting value from this data. This PhD will explore advanced analytics techniques, including machine learning, digital twin modelling, time series analysis, spectral analysis
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epidemiology and machine learning. The scholarship will fund course fees up to the value of home fees*, a tax-free stipend of no less than £20,780 per annum), plus additional support for research expenses
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control system that enhances Annual Energy Production (AEP), reduces mechanical stress, and improves fault detection using machine learning (ML) and physics-based modelling. The candidate will gain hands
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Generative machine learning models have made significant progress in recent years. Typical examples include, for example, high-quality image or video generation using diffusion models (e.g
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specific drug resistance and pathogenesis mutations. The project will combine classical microbial genomics with machine learning and AI analysis approaches to create the most in depth population analysis
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. when do we stop modelling? How do we track / score the quality of the model? What is the required level of quality over time? How can quality be brought to the required level? Can Machine Learning, Large
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, analytical and computer programming skills. Advantage will be given to applicants with experience in one or more of the following: signal processing, deep learning, acoustics, psychoacoustics, acoustic
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace - In Partnership with Rolls-Royce PhD
relevant field such as engineering, computer science, or applied mathematics. Experience or interest in AI, machine learning, or digital systems is beneficial. We welcome candidates from diverse backgrounds