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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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This PhD project focuses on advancing computer vision and edge-AI technology for real-time marine monitoring. In collaboration with CEFAS (the Centre for Environment, Fisheries, and Aquaculture
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, biology, or a closely related discipline Desirable experience: optics and photonics, AI/machine learning, biology, or biomedical sciences Excellent English, analytical, and problem-solving skills UK
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intelligence, NLP, machine learning, or a related field Experience with Python and Generative AI libraries (e.g., Huggingface Transformers) Knowledge of Multimodal Generative AI models and their corresponding
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including machine learning. This research will support the path to net zero flights and there will be opportunities to become involved in practical aspects of fuel system design and testing during their PhD
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. when do we stop modelling? How do we track / score the quality of the model What is the required level of quality over time How can quality be brought to the required level Can Machine Learning, Large
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for their employability in applications. Additionally, machine learning methods need to be applicable to high-dimensional and to noisy data that are typically encountered in real-world applications. The aim of this project
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new Wellcome-funded Imaging Machine learning And Genetics in Neurodevelopment (IMAGINE) lab, in the Research Department of Biomedical Computing. The post will benefit from the extensive and broad
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. You will focus on machine learning, but will be involved in all areas. There are also spinout opportunities. For details: PhD information sheet The team have wide experience studying bumblebee behaviour
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the investigation and realization of improved microwave probe design, data processing, and visualization techniques to provide a robust method of data analysis, flaw characterization and sizing. AI/machine learning