52 machine-learning "https:" "https:" "https:" "https:" positions at University of Sheffield
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“An improved machining temperature prediction model for aerospace alloys: Effect of cutting edge radius and tool wear”, Journal of Manufacturing Processes 133 (2025) 1100–1110. https://doi.org/10.1016/j.jmapro
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, brain-inspired computation. Supervisor Bio Professor Eleni Vasilaki is a computational neuroscientist who has spent years building bridges between biological learning theory and machine learning. Her most
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, advanced statistical 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
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. This PhD project will build directly on this work by using ideas from machine learning—originally developed to study the movement of larger organisms—to understand how bacteria process information in
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to achieve a sustainable wind farm lifecycle by developing methods for high-value reuse of composite turbine blades. Machine learning and non-destructive evaluation techniques will be developed to efficiently
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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: Are you
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intelligence (AI) is changing how we understand and improve healthcare. This project will use the latest advances in AI to develop new tools that can learn from many different types of medical information
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experiments and cognitive modelling. You will focus on machine learning, but will be involved in all areas. There are also spinout opportunities. For details: PhD information sheet The team have wide experience
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species, and the emergence of previously unseen classes. Recent advances in remote sensing and machine learning provide new opportunities to address these challenges, but most current approaches
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, the project proposes to also use machine learning techniques to learn parts of the prior and penalty structure from data in an interpretable way. Examples include mapping liquidity and volatility features to a