129 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Simons Foundation" positions at Ulster University
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portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications. Sound understanding
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Distinction. In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes
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is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications. Desirable Criteria If the University receives a large number of applicants for the project, the following
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appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications. A comprehensive and articulate personal statement Research proposal
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deep learning’s applicability to achieve environmental sustainability. Post-disaster Damage Assessment Using Satellite Imagery and Machine Learning Supervisor Names: Dr. Muhammad Shafi This project will
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mobile health technologies enable the continuous capture of rich, multimodal physiological and behavioural data. These data when analysed with Artificial Intelligence (AI) and machine learning methods can
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natural fibres and bio-resins, combining renewable materials with advanced processing and computer-aided design/simulation. The research aims to create high-performance, sustainable composites with tailored
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Distinction. In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes
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is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications. A comprehensive and articulate personal statement Research proposal of 2000 words detailing aims
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of Qualitative Research, 18(2), 179-198. https://doi.org/10.1177/19408447241260448 Sinclair, S. & McKendrick, J. (2021) Learning from Local Responses to Child Poverty During the COVID Crisis, available at: https