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formulation, which displays striking similarities to that used by the Computational Fluid Dynamics (CFD) community, has inspired the investigators to adopt conventional CFD algorithms in the novel context
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, the project will develop machine learning based solutions for predictive grid analytics (such as grid congestion forecast, asset monitoring, etc.). Based on these results, the project will develop
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part of the Green Algorithms Initiative in the Department of Public Health and Primary Care, one of Europe's leading academic departments of population health sciences. The post will suit researchers
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aims: Develop end-to-end protocols for screening selected foods and nutraceuticals. Create advanced strategies for data integration using tailored algorithms and machine learning approaches. Demonstrate
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specifically addresses the identified challenge by leveraging ML to overcome barriers associated with platform heterogeneity, including differences in resolution, scale, and feature representation. By developing
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a variety of machine learning algorithms trained on these data and, most crucially, will develop and implement techniques for computing the uncertainty in the prediction. The algorithms developed in
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a variety of machine learning algorithms trained on these data and, most crucially, will develop and implement techniques for computing the uncertainty in the prediction. The algorithms developed in
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This research opportunity invites self-funded PhD candidates to develop advanced deblurring techniques for retinal images using deep learning and variational methods. Retinal images often suffer
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experts in smart cities, artificial intelligence, transportation planning, and urban sustainability. We are seeking applicants interested in developing an efficient system’s approach for optimising urban
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is to develop a highly innovative ‘Lab on a Bench (LoB)’ setup, integrated with Machine Learning algorithm, as a high throughput method for screening and developing formulated products that are used in