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develop AI- and deep learning–based computer vision tools to automatically identify and quantify intertidal organisms. Beyond computer vision, it will leverage machine learning for large-scale, data-driven
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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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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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of insect pollinators from large-scale photographic and video datasets. The research will integrate ecological fieldwork, computer vision and stakeholder engagement to: 1.Develop and optimise deep learning
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dynamic environments, utilising Vision, Language, and Action (VLA) models. The candidate will focus on designing novel training regimes and/or novel architectures for learning in embodied environments
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. dos Santos is an Assistant Professor (Lecturer) in Computer Vision at the University of Sheffield. His research interests include remote sensing image processing, computer vision and machine learning
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the development and implementation of machine learning (ML), computer vision (CV), large language models (LLMs), and vision-language models (VLM) to automate data extraction and interpretation for productivity
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collection activities. Supervision will be provided by academics from various disciplines specializing in biomechanics, image processing, and computer vision, alongside orthopaedic surgeons and academics.
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at leading international conferences and publish in top-tier journals. The successful candidate will gain advanced expertise in multi-sensor fusion, signal processing, machine learning, and positioning
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validation with end-users. The student will have access to specialised training in quantum security and advanced machine learning. The self-funded nature of the project affords the unique flexibility to pursue