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research. During this period, City has made significant investments in its academic staff, its estate and its infrastructure and continues to work towards realizing its vision of being a leading global
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of Artificial Intelligence). General Requirements Candidates should have (i) a PhD degree in Data Science, Artificial Intelligence, Computer Science, Computer Engineering, or a relevant discipline; (ii) a good
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subject, including: biomedical engineering, sports science, physiology, medical science, data science. The candidate must have good data handling skills and have or be willing to learn computer programming
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to departmental service; and (e) perform any other duties as assigned by the Head of Department or his/her delegates. Qualifications Applicants should: (a) have a PhD degree in Computing, Computer Science, Applied
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journals in the field of large vision models, computer vision, and so on. Additionally, the appointees will be responsible for submitting grant proposals to various funding bodies. Furthermore
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- Demonstration of detailed record-keeping and data documentation - Strong interpersonal skills, including maintaining professional working relationships within a diverse research group. - Computer skills (e.g
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splitting and C–N coupling reactions. Work includes computational modeling of carbon-based materials, conducting simulations to understand reaction mechanisms, and developing and applying machine learning
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(relevant coursework may count towards experience). Histology and microscopy skills. Skills in applicable computer technologies, including Microsoft Office, Adobe Photoshop, and Illustrator. Effective written
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, and inclusion Preferred Qualifications Graduate degree (PhD or equivalent) in relevant discipline or completion of a technical program and a minimum five years of related experience or an equivalent
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Scholarship is awarded annually to outstanding Australian graduates to support them with postgraduate study (master's or PhD) at the world’s best universities. The Scholarship is not limited by age, gender