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will collect and analyse clinical, demographic, diet, and lifestyle data to identify modifiable predictors and stratify patients by malnutrition risk, supporting the development of targeted, personalised
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, and mortality. This will be achieved by linking large existing datasets that contain data on people with diabetes foot disease in the community (such as Diabetes Registries, Diabetes Foot Registries
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align with media consumption of documentary/light entertainment; fictional stories; information; games/gaming; music. It would be advantageous to have experience with: market research qualitative
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palliative care models. It comprises a number of sub-projects including a systematic literature review, a synthesis of existing qualitative data, and the design and analysis of a quantitative preference survey
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Creative arts Design Engineering English language and pathway programs Health Information technology and games Justice Languages Law Mathematics and data science Science Teaching Explore Mid-year entry QUT
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. An additional amount of up to $5,000 is available to support project costs, equipment or research-related travel for site visits, data collection, and/or conference attendance. Eligibility To be eligible
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discipline, complete an expression of interest (EOI). The steps are: Complete the EOI available at Next Generation Graduates Program (NGGP): Sports Data Science & AI - Centre for Data Science (qut.edu.au
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discipline, complete an expression of interest (EOI). The steps are: Complete the EOI available at Next Generation Graduates Program (NGGP): Sports Data Science & AI - Centre for Data Science Peruse
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program information
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interests and educational background a CV your degree certificate or equivalent your English results, and other documents you wish to be considered (grade transcripts, contact information for your references