18 parallel-processing-bioinformatics-"Multiple" PhD positions at Loughborough University
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. These components see applications in the transport, catalysis and bioengineering industries. The research will focus on wet chemical processes and the study of chemical reactions on the component's surface. We will
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as early indicators of anthropogenic and climate-driven change. However, limited understanding of the processes shaping species’ biogeographic distributions constrains our ability to predict ecological
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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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under multiple environmental and socio-economic scenarios. You’ll develop sought-after skills in geospatial analysis, hydrodynamics, sediment transport, machine learning-assisted detection, and hydro
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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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select Programme Ph.D. Sport, Exercise and Health Sciences. Please quote the advertised reference number SSEHS/LJ26 in your application. To avoid delays in processing your application, please ensure
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identification of deterioration processes and assessment of their evolution/extent. Please reach out to the primary supervisor, Prof. Craig Hancock , if you have any questions. Entry requirements: We are looking
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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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transcripts for all completed degree programmes, and a reference to the project ‘CSC-26-WS’. Please find further information on the overall CSC scholarship application process as follows: China Scholarship
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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