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Attributes Experience working with quantitative and textual data, writing computer code, and/or using existing Digital Humanities tools. The successful candidate will be supported to fill gaps in their Digital
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principles, lab measurement, computer vision and ArcGIS, potential fieldwork and UAV flying training. Person specification Experience and/or enthusiastic interest in one or more of the following areas
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, the project accelerates trait data acquisition by applying computer vision to herbarium specimens and field photos, as well as large language models to extract complementary information from literature and
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in AI. Previous publication record in relevant fields: AI, machine learning, computer vision, etc. Previous successful project on a relevant topic. Good knowledge of statistics, probability
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for Real-World Optimisation and AI Applications Brain-Computer Interfaces & their Applications Computational Neuroscience: Reinforcement Learning and Microzones in the Cerebellum Explainable Generative
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include training in computer science, human-computer interaction, etc. A Master’s degree is desirable. In addition, candidates for whom English is a second language should meet the University’s minimum
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap
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challenges which experimentalists must consider – computer simulations of molten salts are therefore a very valuable guide to efficient experimentation. Molten salts have been well-studied using classical
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by detecting and predicting threats such as pests, diseases, and environmental stress in line with the UK Plant Biosecurity Strategy. The project harnesses computer vision, deep learning, and large
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research frontier in computer vision that combines three critical challenges: class imbalance, recognition of rare and unseen species, and dense labelling of high-resolution imagery. The candidate will