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for representing and combining multimodal information over time. Grounded in machine learning, representation learning, and efficient algorithms, the work addresses real-world challenges in sustainable and
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underexplored. To this end, the project will explore the application of the next generation of deep learning algorithms, e.g. self-supervised learning techniques, particularly suited to infrastructure
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. Expect close collaboration with industrial experts and the opportunity to see your algorithms influence aerospace and other high-value manufacturing sectors. Funding and eligibility 3-year, full-time PhD
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within the climate change domain. The techniques are based on statistical and computational approaches, including machine learning algorithms. The project aims first to contribute to the prevention of fake
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. Expect close collaboration with industrial experts and the opportunity to see your algorithms influence aerospace and other high-value manufacturing sectors. Funding and eligibility 3-year, full-time PhD
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collaborative project that spans multiple continents. Your main role will be to develop advanced algorithms for multivariate, multi-resolution time series analysis of wearable and neurophysiological data spanning
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Aim: To predict the rate of hair loss or recovery in people with alopecia using computer vision and Artificial Intelligence (AI) algorithms. Objectives: Automate the hair segmentation process and
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multi-disciplinary, conducting innovative, internationally recognised research in power electronics systems. We specialise in the design and implementation of hardware and control algorithms for real
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to optimise built-environment thermodynamics and occupant comfort by creating predictive AI tools for spatiotemporal heat transfer. Machine learning algorithms will identify energy inefficiencies and propose
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propagation models that incorporate the effects of fire effluents, validated through controlled experimentation. You will develop tomographic inversion methods and anomaly-detection algorithms capable