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of living evidence in APTs. Activities will involve conducting a needs analysis and process map of potential high-value decision points within APTs that would benefit from living evidence. The student will
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of eligibility requirements, including English-language proficiency skills, to undertake a PhD in the Faculty of Arts are available at arts.monash.edu/graduate-research/application-process. Applicants should
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Program application process . When prompted, select “Health Economics” as your area of specialisation. Integrated PhD program commencement: July / August 2026 Applications Close: Sunday 1 February 2026, 11
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applying, note the following: When prompted, select “Health Economics” as your area of specialisation To apply, please follow the Integrated PhD Program application process . All eligible applications will
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, note the following: When prompted, select “Health Economics” as your area of specialisation To apply, please follow the Integrated PhD Program application process . All eligible applications will be
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: Before applying, note the following: When prompted, select “Health Economics” as your area of specialisation To apply, please follow the Integrated PhD Program application process . All eligible
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that are critical of generative artificial intelligence will not be disadvantaged in the selection process, but must still meet all other requirements. The below themes are offered as inspiration for research
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Please follow the 3-step application process here . Application dates for the commencement of the Integrated PhD program in July 2026: Applications open: 1 December 2025 Applications close: 1 February 2026
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. Applicants must show excellent communication and interpersonal skills, and the ability to conduct self-motivated research. They should have either completed or in the process of completing a Bachelors H1
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Inference Tool (GAMBIT) Community to study theoretical frameworks that extend the standard models of particle physics and cosmology, with the aim of uncovering the nature of dark matter, dark forces, and dark