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This project is funded by: Department for the Economy (DfE) Our fully funded PhD scholarships will cover tuition fees and provide a maintenance allowance of £21,000 (approximately) per annum for three years
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. The objective of this PhD project is to develop AI methodologies for the analysis part of condition monitoring (CM) and predictive maintenance (PM). The primary challenge in predictive maintenance lies in
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anticipated that the initial focus of the project will be on transmon design, before extending methods to emerging architectures such as unimons. The PhD will suit a candidate interested in optimisation
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Globally Competitive and Sustainable Economy in Ireland's North West Supervisors: This PhD project offers a unique and timely opportunity to be at the forefront of global transformation, exploring how AI
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position is filled. Required Qualifications: Previous experience in athletic grounds, landscaping and/or turf maintenance. Preferred Qualifications: At least one (1) year of experience in athletic grounds
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for the Economy (DfE) Our fully funded PhD scholarships will cover tuition fees and provide a maintenance allowance of £21,000 (approximately) per annum for three years* (subject to satisfactory academic
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individually and in concert with others; cooperative maintenance of exchange platforms (database, information pages, etc.) summarizing the results of the individual PhD study project in a dissertation within the
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novel nanomaterials for next-generation optical and electronic applications, including integrated optical devices, sensors and actuators, and energy harvesting. For more details, please view https
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, biodiversity monitoring, and climate resilience. The work supports strategic priorities in Environmental Sciences, Software/Cyber. PhD researchers will explore how AI-driven Earth observation, computer vision
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their operational reliability. The PhD student will combine mathematical models, in-house laboratory tests in a wind-wave-current flume (https://research.ncl.ac.uk/amh/ ) and numerical methodology to quantify