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the following link regarding English Language requirements: (https://www.ucc.ie/en/study/comparison/english/ ). Desirable experience includes: Fault detection, condition monitoring, or reliability
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[ed](: https://www.tcd.ie/hr/assets/pdf/academic-hours-public-service-agreement.pdf)ucation, research, and innovation, which has been inspiring generations of thinkers for over 400 years. Post Summary
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[ed](: https://www.tcd.ie/hr/assets/pdf/academic-hours-public-service-agreement.pdf)ucation, research, and innovation, which has been inspiring generations of thinkers for over 400 years. Post Summary
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reputation for teaching and research excellence . For information on moving to Ireland please see www.euraxess.ie Detailed Project Description The University of Galway invites applications for a full-time
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SPEAR Centre: PhD in ‘Long-Range, High Bandwidth Distributed Acoustic Sensing for Fibre Optic Links’
applicants whose first language is not English must provide evidence of English language proficiency as per UCC regulations (https://www.ucc.ie/en/study/comparison/english/postgraduate/). Certificates should
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interdisciplinary research Enthusiasm and willingness to learn new techniques are more important than existing specialisation in the glycosciences. LanguagesENGLISHLevelExcellent Research FieldChemistry » Organic
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Photonics Education and Research (SPEAR) Centre is a €8.5 million cross-border project which will provide ATU and UU access to an all-island network of research groups and industry partners with the strategic
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musicology, sound studies, digital humanities, music pedagogy, and/or creative practice, and should have or be working towards a masters degree in ethnomusicology, musicology, or a related field. Application
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as per Times Higher Education and hosts various research centres. The School is committed to embedding a culture of equality, diversity and inclusion, and to that end welcomes applicants from all
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machine-learning approaches for materials science (e.g., regression, classification, surrogate models, or force-field parametrization using platforms such as PyTorch, TensorFlow, scikit-learn, or equivalent