48 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" positions at University of Sheffield in United Kingdom
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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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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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, 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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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
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Machine Learning techniques. As Research Associate you will have a research leadership role in the group, and will assist in day-to-day supervision of post-graduate research students. You will collaborate
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of the resultant analysis. This will also involve the collection of a curated data set, and the use of Machine learning tools to further enhance the analytical process. Together, this will help expand the use
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with core data analysis and professional skills that are necessary for bioscience research and related non-academic careers. https://www.yorkshirebiosciencedtp.ac.uk Project Description: Join an exciting
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Accessible Tinnitus Notch Noise Therapy via Machine Learning, Acoustic Metamaterials and Additive Manufacturing (with NHS and TinnitusUK) EPSRC Centre for Doctoral Training in Sustainable Sound
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Physics based machine learning algorithm to assess the onset of amplitude modulation in wind turbine noise (with TNEI Group) EPSRC Centre for Doctoral Training in Sustainable Sound Futures PhD