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are deeply committed to environmental stewardship and eager to apply their specialized skills in AI, data analysis, software development, and electrical or energy engineering to make a significant and tangible
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PhD student(s) will join a vibrant team of postdocs, academics, and up to four PhD students working collaboratively across modelling, qualitative fieldwork, and optimisation techniques. PhD Research
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qualitative research methodologies including research skills and some experience with the use of qualitative data software such as NVivo. The candidate: Must meet all Curtin University HDR enrolment
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compartmental models for RSV developed within the STAMP-RSV program by tailoring an established software library for individual simulation to the Australian RSV transmission context. Information to parameterise
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biological tissues. Expertise in Multiphysics simulation software (e.g., COMSOL) and programming languages (e.g., MATLAB, Python, C++). Experience with biophotonics applications, such as endoscopy, optical
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. • Proficient computer skills, including competence in the use of MS Office and other software packages, especially word processing, database and spreadsheet skills. About Swinburne University of Technology
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. Demonstrated high level of computing skills, including competency with qualitative analysis software (NVivo), spreadsheets and data management systems. Experience in contributing towards producing conference
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the structure present in food systems dictates functional aspects such as digestion and release of nutrients. Working alongside other postdocs and students focused more on biological aspects of these processes
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record in medical research and innovation. The project will benefit from access to advanced computing facilities and specialized software for deep learning model development and CT scan analysis
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, conducting interviews and focus groups, developing and managing surveys, and analysing qualitative and quantitative data including skills with appropriate software would be valuable. An interest in mental