27 machine-learning-"https:"-"https:"-"https:"-"https:" positions at Trinity College Dublin
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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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of the role is to provide Trinity students with academic learning services and to equip them with world-class academic and graduate skills for their studies and beyond. A key component of the role is to work
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machine-learning models to investigate how brain, environment and social factors influence cognition, ageing and neurodegenerative disease. The role will contribute primarily to WP6 and related analytical
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the Teaching and Learning IT Team, overseeing software deployment and system administration for computer labs and BYOD environments in E3LF, and secondarily across campus and remotely. They will manage and
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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
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transferable insights with the potential to inform sectoral thinking and practice. (2) An impact evaluation of the newly established Learning Innovation & Research Hub. College has established this Hub
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and how they have contributed to the learning process of others. 2. Their ability to work proactively and examples of problem resolution. 3. Their views on the future of anatomy education at
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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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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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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