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is seeking a PhD student to join an ongoing Royal Society–Research Ireland University Research Fellowship project focused on Machine Learning for the Design of Additively Manufactured Two-Phase Heat
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Machine Learning within the School of Medicine at the University of Limerick. This is a methodologically focused PhD for candidates with strong quantitative backgrounds who wish to develop novel statistical
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neuroimaging methods to better detect disrupted function following neonatal brain injury, and identify new more energy efficient learning algorithms that could reduce the economic and environmental cost of AI
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this pathogen to cause infections, survive attacks by innate immune cells (macrophages and neutrophils), and develop resistance to antifungal agents (see selected publications below). The PhD student will learn
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behaviour and electrically tunable interfaces in 2D heterostructures. Methods include density functional theory (DFT), atomistic simulation, high-performance computing, and machine-learning-assisted materials
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of compensating for nonlinear PA characteristics under dynamic operating conditions. Advanced machine learning and neural network approaches will be explored to improve linearization performance while reducing
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(IIoT) sensors, real-time data analytics, machine learning algorithms, and Digital Twin technologies to monitor equipment health, predict failures before they occur, and recommend optimal maintenance
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information on fees. There will be a requirement to teach in undergraduate laboratories and tutorials (144 h per year) as part of the scholarship. Find out more about studying at UCD here: https://www.ucd.ie
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following areas: Robotics and Autonomous Systems Artificial Intelligence and Machine Learning Wireless Communication Systems Ultra-Wideband (UWB) Technologies Industrial Automation and Industry 5.0 Industrial
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catalysts used in chemical processes. The successful candidate will combine state-of-the-art quantum chemical modelling alongside machine learning techniques and contribute to the development of predictive