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Field
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nano-computed tomography, and scanning electron microscopy design and implement deep learning models to enhance resolution of large field-of-view imaging techniques integrate imaging data across
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of reinforcement learning or agent-based systems. LanguagesENGLISHLevelExcellent Research FieldComputer science » Computer systemsYears of Research Experience1 - 4 Additional Information Benefits • Full funding
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the project is the development of AI-based pipelines for detecting, segmenting, and classifying lichen communities. Convolutional neural networks (e.g., U-Net, DeepLab) and machine-learning algorithms (e.g
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multimodal data, ultimately uniting rigorous machine learning foundations with biological discovery. Project details This PhD project will contribute to the development of generative models for multimodal data
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series data. Large data sets come with significant computational challenges. Tremendous algorithmic progress has been made in machine learning and related areas, but application to dynamic systems is
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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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centered around a unique, open-source digital platform enriched with data and powered by domain knowledge-based advanced machine learning and artificial intelligence capabilities. By introducing a Digital
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programme aims to advance fundamental understanding of heat transfer and turbulence physics in wall-bounded flows through numerical simulations, data-driven modelling, and machine learning techniques. Key