49 machine-learning "https:" "https:" "https:" "https:" uni jobs at University of Sheffield
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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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. Into the second year, the project moves toward methodology refinement and Machine Learning integration. The student will execute a more ambitious cycle with a complex alloy system and integrate machine learning
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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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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 from industry
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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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BSM processes. This will involve taking a lead role in developing dedicated software frameworks, including the implementation of machine learning techniques. A long-term attachment (6-12 months) and
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develop our composite machining team. In this pivotal role, you will lead a diverse group of staff, fostering their skills in CNC programming and operation to efficiently undertake research relating
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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
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methods and the potential to develop pioneering reconstruction and calibration techniques involving machine learning. The PhD will prepare equally well for a career in industry and academia. The candidate
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digital-first principles. By fusing materials science, machine learning, and advanced simulation, the project offers an exciting opportunity to redefine how we engineer and deploy functional surfaces