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Field
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barriers: a large input modality gap, as network data consists of diverse, non-textual formats like time-series metrics, graphs, and scalar values; the inefficiency and unreliability of answer generation
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), machine learning (ML), deep learning (DL) and Data science methods for medical image analysis, to autonomously grade the fundus images from large datasets. This will be supported by Professor Neil Vaughan
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mass spectrometry experimental data, creating a unique multi-stranded methodology to map out free energy landscapes associated with protein folding in environments spanning gas-phase to microsolvation
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propagate through bacterial communities while deactivating AMR genes. However, current designs are limited by scalability and complexity. This project aims to overcome these limitations by integrating large
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Science, or a closely related field. Proficiency and interest in programming languages such as Python, MATLAB, or similar, used for large-scale data processing and model development. Excellent written and
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the slow dynamics leading to high-quality modelling of currently inaccessible experimental quantities. About HetSys: Harnessing Data, Modelling and Simulation for Real‑World Impact HetSys (Centre
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contribute to pioneering computational tools that extend far beyond potassium. About HetSys: Harnessing Data, Modelling and Simulation for Real‑World Impact HetSys (Centre for Doctoral Training in Modelling
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acoustic data, has shown transformative potential in domains such as healthcare, environmental monitoring, and autonomous systems. However, most advances rely on large datasets and computationally intensive
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project will develop novel methods for modelling and controlling large space structures (LSSs), so that they can be reliably utilised in space-based solar power (SBSP) applications. Working with leading
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decision-making. Examples include crowd management and large-scale communication networks based on cellular or wireless sensors. For instance, during mass gatherings such as the sport matches (e.g