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and apply state-of-the-art modelling, characterisation, and machine learning techniques to understand how batteries behave and age. Collaborating with project partners, you’ll turn these insights
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2025. We seek to recruit a Research Associate specialising in statistical modelling and machine learning to join our multi-university multi-disciplinary team developing a groundbreaking technique based
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signal processing schemes using machine learning methods and knowledge of inverse scattering methods (nonlinear Fourier transform). About us: AiPT is one of the world’s leading photonics research centres
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develop statistical and machine learning models to identify and validate predictive biomarkers of resistance evolution in Pseudomonas aeruginosa lung infection. As part of this work, the postholder will
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more effective screening and therapy. The postholder will focus on developing and applying advanced computer vision and machine learning methods for multimodal imaging and real-time analysis in
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of the study. The post holder will be based at the University of Edinburgh’s Centre for Cardiovascular Science, a leading centre combining world-leading cardiovascular disease research, state-of-the-art machine
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for manufacturing operations. Process control: process modelling, control, and optimization, with applications in chemical and pharmaceutical manufacturing; data-driven modelling and machine learning applications in
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, artificial intelligence/machine learning, digital twins, and blockchain technology for operations and maintenance. This position is part of the Maritime Future Fuels Training Plan project, which aims
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they are kept fully up to date with progress and difficulties in the research projects. It is essential that you hold a PhD/DPhil in a quantitative discipline (e.g. Statistics, Machine Learning, Biostatistics, AI
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, artificial intelligence/machine learning, digital twins, and blockchain technology for operations and maintenance. This position is part of the Maritime Future Fuels Training Plan project, which aims