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patients survive. However, analysing these samples can be slow and take a lot of work for doctors. The goal of this PhD project is to create AI and machine learning techniques to more accurately and quickly
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. Identifying Key Predictors of Cognitive Dysfunction in Older People Using Supervised Machine Learning Techniques: Observational Study JMIR Med Inform 2020;8(9):e20995 Tobin et al. Co-Clustering Multi-View Data
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. This project is a 4-year PhD project with enhanced training and 3+ month placement, which is fully funded by UKRI BBSRC through the NI Landscape Partnership in AI for Bioscience (NILAB) Programme, delivered by
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for Predicting Dairy Cattle Methane Emissions: From Traditional Methods to Machine Learning, Journal of Animal Science, Volume 102, 2024 [3] Ruff W.E., Greiling T.M. & Kriegel M.A. Host–microbiota interactions in
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Cognition: Implementation of machine/deep learning approaches that allow robotic systems to personalise their assistance based on individual user patterns, preferences, and requirements. Potential application
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researcher and the PHA by providing expertise in data analytics, statistical analysis and machine learning, and the PhD researcher will also be supported by the Doctoral College training resources at Ulster
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learn about the fundamental laws of nature. In this PhD project the student will develop new machine-learning tools to improve our understanding of the processes being measured at the LHC, and in turn
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across different stages of the machine learning pipeline, but their selection and application for specific use cases need further investigation. This PhD project will: * Provide a taxonomy of XAI methods
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Summary Real-world environments are often non-stationary. Machine learning systems such as classifiers used for pattern recognition often make the stationarity assumption in input data distribution
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to ensure both the privacy of data and the security of the network itself. Federated learning (FL), a decentralised machine learning paradigm, presents a promising solution to these challenges. In contrast