51 machine-learning "https:" "https:" "https:" "https:" "https:" positions at University of Sheffield in United Kingdom
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combination of physics-based, and data-driven AI-based approaches employing neural-networks and machine learning, this project will develop and validate a multi-time scale DT concept for advanced condition
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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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very difficult. In other large scale machines (e.g. hydro-electric power stations, ships propeller bearing) sliding type or ‘hydrodynamic’ bearings [4] are much more common. There is increasing interest
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work with the UK semiconductor industry. The studentship represent a unique opportunity to be trained in the epitaxy process and to work in an emerging and exciting area of combining AI/machine learning
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experience, or keen to learn the research knowledge in power systems, cyber-physical systems, computer science, information and communications technologies, and computing and data platforms. The perspective
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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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-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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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