19 machine-learning "https:" "https:" "https:" "https:" "EMBL EBI European Bioinformatics Institute" PhD positions at Loughborough University
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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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of separating fire-induced signatures from natural environmental variability (weather, canopy changes, tree motion) and fluctuations in the SoO sources themselves. Machine-learning methods will help improve long
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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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are available on the International website (http://www.lboro.ac.uk/international/applicants/english/ ). Funding information: The studentship is for 3 years and provides a tax-free stipend of £20,780 per annum (in
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selection criteria will be used by academic schools to help them make a decision on your application: https://www.lboro.ac.uk/study/postgraduate/apply/research-applications/studentship-assessment-criteria
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subject to final approval by the University. The following selection criteria will be used by academic schools to help them make a decision on your application: https://www.lboro.ac.uk/study/postgraduate
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designed to meet multiple needs in marine biodiversity monitoring. The project aims to develop embedded novel deep learning and computer vision algorithms to extend the system’s capabilities to classify
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an advantage. English language requirements: Applicants must meet the minimum English language requirements. Further details are available on the International website (http://www.lboro.ac.uk/international
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(computer vision technologies). The interdisciplinary nature of this PhD will require the integration of environmental science, engineering, and community science methodologies. Supervisors: Primary
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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