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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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Digital manufacturing, Industry 4.0, or cyber-physical systems o Product design for disassembly, remanufacturing, or recycling o Data analysis, AI, or machine learning applied to engineering systems
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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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, an innovative initiative funded through the Strategic Alignment of Teaching and Learning Enhancement (SATLE) programme. The successful candidate will contribute to research and planning that enhances doctoral
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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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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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over 30 industry partners to learn about their work and future career opportunities Have the opportunity to explore commercialisation of research outcomes through a spin-out company Join a programme
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candidates with a first-class honours bachelor's degree may be considered Strong interest in laser physics, photonic integration, and/or free space optics Good laboratory skills and willingness to learn new