107 development-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" PhD positions in Australia
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government stakeholders, enhancing career prospects and providing real-world experience. Professional development: mentorship from leading experts and the opportunity to showcase findings in high-impact
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formulation development and cell culture studies. Motivated, enthusiastic with attention to detail, you will have the ability to communicate effectively with various stakeholders and have the ability to
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development of machine learning techniques that may be used to triage, classify, and otherwise process material of a distressing nature (such as child exploitation material). It will involve the use of deep
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development of machine learning techniques that may be used to triage, classify, and otherwise process material of a distressing nature (such as child exploitation material). It will involve the use of deep
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Charles Sturt University | Charles Sturt University, New South Wales | Australia | about 10 hours ago
development of new knowledge in viticulture and wine. Eligibility As per Charles Sturt University's standard eligibility requirements for entry to a PhD. Award value This scholarship provides a
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package totalling $46,000 per annum (2024 rate) a four-year Research Training Program (RTP) Fee-Offset a four-year project expense and development package of $13,000 per annum a three-month industry
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care about creating a positive, respectful, and stimulating environment. We value communication and collaboration and a workplace that promotes learning and development for all employees. Our team is
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eligible applicants must: • Be eligible to study full time PhD at UniSQ. • Have strong publications record. • Excellent communication skills. • Strong AI development skills, demonstrated through prior
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eligible applicants must: • Be eligible to study full time PhD at UniSQ. • Have strong publications record. • Excellent communication skills. • Strong AI development skills, demonstrated through prior
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learner agency. The rise of Generative AI and large language models (LLMs) further intensifies these challenges, calling for the development of responsible AI systems that are transparent