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confined battery geometries. Advanced modelling—including computational fluid dynamics (CFD) and transient thermal analysis—is required to accurately capture heat flux distributions, temperature uniformity
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for many developments in model theory, starting with strong minimality, as well as providing some of the most important applications where model-theoretic tools are applied to algebraic geometry. After
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data are needed to enhance our understanding of sources, pathways and impact of litter. Cefas is developing a visible light (VL) deep learning (DL) algorithm and collected a large 89 litter category
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approach that integrates machine learning algorithms, blockchain technology, and IoT devices with digital twin systems. The scientific objectives of the project are as follows: Objective 1: Investigate how
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to eruptive activity. These transitions pose significant challenges to hazard management (1). Physical parameters, such as the location, geometry, and size of the volcanic system, and their changes over time
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can feed directly into precision surgery algorithms and clinical trials. Few PhD projects offer such a clear line of sight from variant to mechanism to clinical translation. Located on the thriving
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of short-axis MR image sequences. Training You will be based at the Vision Computing Lab within the School of Computing Sciences, which specializes in deep learning for medical image analysis and neural
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on such jets — although their detailed properties remain unexplored. In this project, we will develop new computational methods to compute and characterize these novel wave solutions, with particular emphasis
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community, supported by cutting-edge research facilities and excellent training opportunities. The Norwich Research Park Biosciences Doctoral Training Programme (NRPDTP) is offering fully funded studentships
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, and edge computing, AASs are evolving into smart, interconnected solutions for addressing the dynamic challenges of modern cities. This project investigates how to coordinate these multifunctional