565 machine-learning "https:" "https:" "https:" "https:" "https:" "Cardiff University" positions at University of Sheffield
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: https://www.sheffield.ac.uk/postgraduate/phd/apply/english-language. How to apply: Please see this link for information on how to apply: https://www.sheffield.ac.uk/cbe/postgraduate/phd/how-apply. Please
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://www.sheffield.ac.uk/sgs to learn more. Please apply for this project using this link: https://www.sheffield.ac.uk/postgraduate/phd/apply/applying Funding Notes Available for self or externally funded students only
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, water quality and meteorological datasets routinely collected by water utilities. The student will have the opportunity of using state-of-the-art machine learning methods (predictive analytics) to analyse
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, increased carbon dioxide, rainfall and snow regime change and how these impact the biodiversity of ecosystems, and the capacity of ecosystems to cycle carbon and nutrients (https://sites.google.com/a
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at the University of Sheffield. There will be many opportunities to collaborate with ongoing work in the lab. For more details see http://www.alisonewright.co.uk. Applicants are strongly encouraged to contact Dr
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-standard queries. Essential Application/interview Good understanding of computer operating systems (Windows and Mac OS) and desktop software (MS Office, web browsers) and an interest in learning new
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of application review and, where applicable, interview. Eligibility and application guidance can be found here: https://slt-cdt.sheffield.ac.uk/apply For an informal discussion about your application
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expertise in machine learning, soil microbiomes, microbial 3D printing and biophysics, our team has access to a broad spectrum of techniques and practical know-how. This is therefore an exciting opportunity
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Forecasting the Future of Biodiversity: Cutting-Edge Approaches to Population and Community Dynamics
: How can tools like passive bioacoustics revolutionize wildlife monitoring? We offer cutting-edge training in statistical modelling, machine learning, and ecological forecasting, and our lab works across
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hydro-climatic conditions govern vegetation behaviour, and how vegetation impairs the functioning of drainage and water-management assets. Using advanced geospatial modelling, machine learning and digital