25 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"UCL" positions at Trinity College Dublin in Ireland
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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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artificial intelligence may fundamentally reshape learning, universities, knowledge creation and professional formation. The postholder will be expected to play a formative role in shaping this initiative from
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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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. Examples of their understanding of and involvement in anatomy teaching modalities and how they have contributed to the learning process of others. 2. Their ability to work proactively and examples
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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