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PhD in Computer Science: Sustainable AI for Plant Species Recognition in Tropical Forests School of Computer Science PhD Research Project Directly Funded Students Worldwide Dr Jefersson Alex dos
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application of computational tools for the early detection and deconstruction of chromosomal instability in cancer” For further information about the research group, including their most recent publications
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unit and then pre-processed data used as the input of the deep learning algorithm. The research will employ the SafeML tool (a novel open-source safety monitoring tool) to measure the statistical
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, the project will develop algorithms for ecological sensing, adaptive motion planning, and energy optimisation under real-world constraints. Scaled experiments and high-fidelity simulations will validate system
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mission. You will: Help collate data resources relevant to suicide and self-harm. Develop new machine learning methodologies (from artificial neural networks, decision trees, evolutionary algorithms and
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to train tomorrows leaders in earth and environmental science. For further details about the programme please see http://nercgw4plus.ac.uk/ For eligible successful applicants, the studentships comprises
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data are needed to enhance our understanding of sources, pathways and impact of litter. Cefas is developing a visible light (VL) deep learning (DL) algorithm and collected a large 89 litter category
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approach that integrates machine learning algorithms, blockchain technology, and IoT devices with digital twin systems. The scientific objectives of the project are as follows: Objective 1: Investigate how
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implementing AI algorithms to deliver safer and more efficient care. The student will have access to a unique training programme in AI in healthcare and health data science as well as a wide range opportunities
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) develop novel performance metrics combining accuracy and explainability, to be tested across different AI model types; (2) devise new algorithms for selecting models optimised for holistic performance